Geomatica OrthoEngine · Introduction Geomatica OrthoEngine Paaggee G88 a iPPCCII Geeoommatticcss You should copy this data to your hard disk. Note This training manual can be used

Geomatica OrthoEngine

Course exercises

Geomatica Version 2018

Geomatica OrthoEngine Course exercises

PPaaggee iivv PPCCII GGeeoommaattiiccss

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Contents

Introduction 7

About this training guide 7 Project files in OrthoEngine 8 Geospatial data structures 9 GDB technology in Geomatica 9 PCIDSK and Geomatica 10 PCIDSK file format 11 Starting OrthoEngine 11

Module 1: Project Setup 13

About this module 13 Lesson 1.1 - Setting up satellite projects 16 Lesson 1.2 - Setting up aerial photograph projects 22

Module 2: Computing the math model 33

About this module 33 Lesson 2.1 - Collecting ground control points 35 Lesson 2.2 - Collecting tie points 50 Lesson 2.3 - Calculating the math model 60

Module 3: DEM operations 71

About this module 71 Lesson 3.1 - Creating epipolar images 72 Lesson 3.2 - Extracting and geocoding the DEM 78 Lesson 3.3 - DEM Editing 85 Lesson 3.4 - Building and merging DEMs 95

Module 4: Orthorectification 107

About this module 107 Lesson 4.1 - Generating the orthorectified images 109

Module 5: Mosaicking 115

About this module 115 Lesson 5.1 - Defining a mosaic Area 118 Lesson 5.2 - Manual mosaicking 124 Lesson 5.3 - Automatic mosaicking 137

Module 6: OrthoEngine componentization 144

About this module 144 OrthoEngine components 144 Modeler 144 Lesson 6.1 - Data input and GCP collection 145 Lesson 6.2 - Project creation and tie point collection 156 Lesson 6.3 - Automatic GCP collection and mosaicking 164

Appendix A 171

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Minimum GCP requirements 171

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Introduction

About this training guide

Welcome to the Geomatica OrthoEngine training course. This course is designed for

new and experienced users of remote sensing and digital photogrammetry software.

In this course you will master the basics of GCP edition, tie point collection, DEM

extraction, orthorectification, and mosaicking. In addition, you will discover some of

the features in OrthoEngine componentization, running components in batch mode, automatic image-to-image registration, and more.

There are six modules in this training manual. Each module contains lessons that

are built on basic tasks that you are likely to perform in your daily work. They

provide instruction for using the software to carry out essential processes while sampling key OrthoEngine applications and features.

The scope of this guide is confined to the core tools available in Geomatica

OrthoEngine; however, some remote sensing concepts are reviewed in the modules and lessons.

Each module in this book contains a series of hands-on lessons that let you work

with the software and a set of sample data. Lessons have brief introductions followed by tasks and procedures in numbered steps.

The following modules are included in this course:

Module 1: Project Setup

Module 2: Computing the math model

Module 3: DEM operations

Module 4: Orthorectification

Module 5: Mosaicking

Module 6: OrthoEngine componentization

Module 1 and Module 2 deal with the Data Preparation stage and include exercises

for setting up a project, loading images, adding ground control points (GCPs),

collecting tie points (TPs), applying sensor models, and examining reports.

Module 3 is concerned with Data Extraction.

Module 4 and Module 5 deal with the Data Correction stage and include lessons for

setting up and generating ortho images, defining a mosaic area, manual mosaicking, and automatic mosaicking.

Module 6 examines how to set up and run OrthoEngine components in batch mode

using Modeler.

The data you will use in this course can be found on the Training Resources

webpage - https://support.pcigeomatics.com/hc/en-us/categories/200255819-Training-Resources

Introduction Geomatica OrthoEngine

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You should copy this data to your hard disk.

Note This training manual can be used to set up any kind of

image or airphoto project. Modules 2 through 6 apply to

any kind of data. Substitute the file names in the manual

with your own data file names. The OrthoEngine

Workbook will allow you to explore other math models

using a variety of datasets.

Project files in OrthoEngine

To use OrthoEngine, a project file must be created. Project files are ASCII text files

with a .prj extension. When you create a project, you specify a math model, the

mathematical relationship used to correlate the pixels of an image to correct

locations on the ground accounting for known distortions. You also specify the

coordinate system and a datum for the project. All data in the project must use this

coordinate system and datum.

A typical project file contains:

Project information

Camera calibration

Projection setup

Photo or image information including the:

File name and location of each input photo or image

File name of the output ortho

File name of the DEM associated with the ortho

Background value for the DEM

Photo or image channel where data is stored

Ortho channel where data is stored

Clip area coordinates

Status of the bundle adjustment

Status of the ortho

Fiducial mark and principal point locations for all photos

Ground control point and tie point locations with elevation data

Exterior orientation values

The next three sections of the project file list information about:

Cutlines created in the mosaicking step

Look-up tables generated to match the images radiometrically during the

mosaicking step

Preferences set up for the appearance of the cursors and labels for such things

as GCPs and TPs associated with the working photo or image

The final section of the project file lists the parameters that were set up during the ortho generation step. The information includes the following:

Elevation units

Resampling method used for the orthorectification

Amount of memory allocated for the orthorectification

Output resolution

Geomatica OrthoEngine Introduction


Output mosaic file name

Upper left bounds of the final mosaic

Lower right bounds of the final mosaic

Geospatial data structures

Data for geospatial applications are stored in complex files that are often

incompatible with specific software packages and operating systems. Files can

come in hundreds of different formats and in most geospatial applications often

require considerable preparation or preprocessing before they can be combined in a work project.

Most geospatial formats store image data in one file and supplementary data, such

as bitmaps, vector layers and metadata in another file using different file

extensions for each data type. Updating and maintaining complex datasets made up of many file types can be a difficult and error-prone process.

PCI Geomatics has developed two unique technologies that make data

management easier: Generic Database (GDB) technology and the PCIDSK file

format. The following sections explain how GDB technology and the PCIDSK format work in Geomatica to make your data management easier.

GDB technology in Geomatica

Generic Database (GDB) technology is key to Geomatica applications. GDB makes it

possible to view and integrate geospatial data from more image formats than any

other geomatics software. It allows you to use as much data as you require in your

work and to combine images of any data type, resolution, and size. You can use

image files, with their accompanying metadata, in the same georeferenced viewer even after combining various file formats and data types.

The list of file formats that GDB uses is constantly under development; there are

currently close to 150 usable geospatial file types. Many popular formats such as

ARC/INFO, GeoTIFF, JPEG2000, AutoCAD, and MicroStation are fully supported.

GDB operates behind the scenes in Geomatica applications. The illustration below

shows a file selection window for Geomatica Focus. When you click the Files of type

box, you can see the list of file formats that can be opened directly into a Geomatica application.



Figure 7. GDB in Geomatica

With GDB technology, you can work through a mapping project by assembling

raster and vector data from different sources and different file formats without the

need to preprocess or reformat the data. Together, GDB and Geomatica read, view, and process distribution formats, and read, edit, and write exchange formats.

PCIDSK and Geomatica

PCIDSK files contain all of the features of a conventional database and more. They

store a variety of data types in a compound file that uses a single file name

extension. The image data are stored as channels and auxiliary data are stored as

segments. All data types are stored together in the file using .pix as the file name

extension. The data type and format of the component determines whether

searching, sorting and recombining operations can be performed with the software application tools.

In PCIDSK files, images and associated data, called segments, are stored in a

single file. This makes it easier to keep track of imagery and auxiliary information.

Geomatica OrthoEngine Introduction


PCIDSK file format

Using a single file for each set of data simplifies basic computing operations. Since

all data is part of the same file you can add or remove parts of it without having to

locate, open, and rename more files.

PCIDSK files are identical in all operating environments and can be used on

networked systems without the need to reformat the data.

PCIDSK files Conventional files

Figure 8. Conventional files and PCIDSK files

Starting OrthoEngine

Windows

To start OrthoEngine on Windows systems

1. Click the Start button, click Programs, click PCI Geomatics, click

Geomatica 2018 and then click OrthoEngine 2018. The OrthoEngine window opens.

Image Files

Training site files

Histogram files

Image channels

Training site segments

Histogram segments

Saved Separately using differentSaved as a single file using the file namefile name extensionsextension .pix



Figure 9. OrthoEngine window


Module 1: Project Setup

About this module

Module 1 has two lessons:

Lesson 1.1 - Setting up a Satellite project

Lesson 1.2 - Setting up an Airphoto Project

Starting a Project

To start a new project you need to select a math model. A math model is a

mathematical relationship used to correlate the pixels of an image to correct

locations on the ground accounting for known distortions. The math model that you

choose directly impacts the outcome of your project. To achieve the results that

you are looking for, you need to understand what the math models do, what the

math models require to produce an acceptable solution, and which math model to

use with your project. You can use one of six math modeling methods:

Aerial Photography

Optical Satellite Modeling

Radar Satellite Modeling

Polynomial

Thin Plate Spline

Adjust Orthos

The OrthoEngine Workbook includes exercises and detailed descriptions of the

options available for the different math models.

In this manual, an Aerial Photography Modelling project is used to examine the

Data Preparation, Data Extraction and Data Correction stages of an OrthoEngine project.

Module 1: Project Setup Geomatica OrthoEngine


Math Modelling Methods available in OrthoEngine

Project stage Aerial

photography

Satellite:

Toutin's

model

Satellite:

ASAR

PALSAR

RADARSAT

Satellite:

Low-

resolution

AVHRR

Data Preparation Digital and

analog photos supported

Camera calibration information is required

Exterior

orientation can be calculated from GCPs/TPs, GCPs/TPs and GPS/INS, or with GPS/INS only

Sensor model is calculated for

the block of air photos in the project

Images must

be read to pix format - orbital

segment is created within the pix file

A minimum

number of GCPs must be collected to calculate the sensor model

Tie points can be collected for overlapping images

Sensor model

is calculated for the block of images

Images must



GCPs are

optional, 1 or 2 can be collected to improve the model

Tie points cannot be collected

Sensor model is calculated

for each individual image in the project

Images must



GCPs are

optional, 1 or 2 can be collected to improve the model


Data Extraction Import and

build DEM

options are available

A DEM can be

extracted from stereo photos

Import and

build DEM


A DEM can be

extracted

from stereo

images

Import and

build DEM


A DEM can be

extracted

from stereo

images

Import and

build DEM


Data Correction A raster DEM

is required for

orthorectification

Manual and

automatic mosaicking

A raster DEM

is required for

orthorectification

Manual and


A raster DEM

is required for

orthorectification

Manual and


Orthorectificat

ion performed

within PCI GeoComp



Polynomial Thin Plate

Spline

Rational

Functions:

Compute

from GCPs

Rational

Functions:

Extract

from image

file

Any digital image

in a GDB supported format can be input into a project

GCP coordinate contains only x and y values


Sensor model is calculated for each individual image in the project

Any digital

image in a GDB supported format can be input into a project

GCP coordinate must have x, y and z values


Sensor model is calculated for each individual

image in the project

Any digital

image in a GDB supported format can be input into a project

GCP coordinate must have x, y and z values


Sensor model is calculated for each

individual image in the project

Input files in

GeoTiff or NITF formats with associated RPC metadata

GCPs are optional, 1 or 2 can be

collected to improve the model

Tie points can

be collected for overlapping images

Sensor model is calculated for block of images

Import and

build DEM options are available

Import and


Import and


A DEM can be extracted from stereo images

Extracted elevations referenced to ellipsoidal heights

No DEM needed

for geometric correction

Manual and automatic mosaicking

No DEM needed

for geometric correction


A raster DEM is

required for orthorectification


A raster DEM is

required for orthorectification




Lesson 1.1 - Setting up satellite projects In this lesson you will:

Create a project

Set projection parameters

Add the data to the project

Save your project

This lesson describes how to set up a pair of satellite images as part of the Data

Preparation stage. To set up your images, you require:

Optical images in their raw data format, or

Radar images in their raw data format

Map projection information

Note The processing steps for optical and radar projects are the

same.



Checking the satellite orbital modeling workflow

Figure 10. Satellite orbital modeling workflow



Creating a project

OrthoEngine works on a project-by-project basis. Therefore, you need to open an

existing project or create a new project before you gain access to the functions within OrthoEngine.

In this lesson, you will set up a new project using optical data. The procedures are

the same for working with radar data.

To create a new project

1. On the OrthoEngine window in the File menu, click New

The Project Information window opens

2. Click Browse

The File Selector window opens

3. Locate the SPOT folder

4. In the File name box, enter spot.prj and click Save

The path and filename appear in the File name box in the Project Information

window.

5. In the Name box, enter SPOT Project

6. In the Description box, enter SPOT ortho project for Irvine, CA

7. For the Math Modelling Method, select Optical Satellite Modelling

8. Under Options, select Toutin’s Model

Note that SPOT is listed in this category.

9. Click OK

The Project Information window closes and the Set Projection window opens.

Figure 11. Project Information window



Figure 12. Set Projection window

Setting the projection parameters

A projection is a method of portraying all or part of the earth on a flat surface.

Output Projection defines the final projection for ortho images, mosaics, 3-D features, and digital elevation models (DEMs).

GCP Projection defines the projection of your source of ground control information

used during either manual ground control point (GCP) collection or when importing

GCPs from text file. If you collect GCPs from a geocoded source, the coordinates

are reprojected to the GCP Projection and saved into the project file.

If you collect GCPs from multiple sources, you can change the GCP Projection to

match each source using the Set Projection window. Using different projections

increases processing time during orthorectification, but it means that you do not have to reproject your ground control prior to using it in OrthoEngine.

The projection information needs to be set at the beginning of each project. In the

Set Projection window, enter the projection information for the Irvine area.

Output Projection

To enter the Output Projection parameters

1. From the list to the left of the Earth Model button, select UTM

The Earth Models window opens.

2. Click the Ellipsoids tab

3. Select E000 and click Accept

The UTM Zones window opens.

4. Select Zone 11 and click Accept

The UTM Rows window opens.

5. Select Row S and click Accept

6. In the Output pixel spacing box, type 10

7. In the Output line spacing box, type 10

GCP Projection

To enter the GCP Projection parameters

1. Under GCP Projection, click Set GCP Projection based on Output

Projection

The GCP Projection adopts the same settings used for the Output Projection.



2. Click OK

The Set Projection window closes.

Note If you wish to modify the projection information, reopen

the Set Projection window.

Changes to the projection mid-project will make any

existing ortho photos invalid.

Adding images to the project

For most sensors, OrthoEngine uses the Read CD-ROM option on the Data Input

toolbar to read the raw satellite data, save the imagery into a PCIDSK file, and add a binary segment containing the ephemeris data (orbit information) to the file.

Note If you save satellite data from the CD onto a hard disk

before reading it to a PCIDSK file, it is important that you

maintain the naming structure of the folders as they

appeared on the CD. If the structure or folder names are

changed, you may encounter errors.

For this lesson, the data have already been read to PCIDSK format. In this case,

you will use the Read PCIDSK file option from the Data Input toolbar.

To import satellite data from a PCIDSK file

1. On the OrthoEngine window in the Processing step list, select Data Input

A new toolbar with four icons appears on the OrthoEngine window. With the

Data Input toolbar, you can input data from either CD-ROM, PCIDSK file, or a

generic image file.

Figure 13. Data Input toolbar

2. On the Data Input toolbar, click Read PCIDSK file

The Open Image window opens.

3. Make sure the Uncorrected images option is selected

4. Click Add Image

The File Selector window opens.

5. Locate the SPOT folder

6. Hold down the CTRL key, select spotleft.pix and spotright.pix and click

Open

The Multiple File selection message window opens. This window indicates the

total number of files that are detected, and the total number to be loaded into

the project.



7. Click OK

The File Selection window closes. Both SPOT images are now part of your

project.

Saving the project

To save your project file

2. From the File menu in the OrthoEngine window, click Save.

The spot.prj file is saved in the SPOT folder.

In addition, OrthoEngine automatically creates a backup file every 10 minutes. The

backup file uses the same file name as your project file, but with a .bk extension.

Note If you need to revert to the backup file, rename the

backup file so that it uses the .prj extension. OrthoEngine

can load this project file in the normal way.

To change the settings of the backup option

1. On the OrthoEngine window, click the Tools menu and select Options

2. Under the General category, make sure the Automatic backup is checked

3. Type the number of minutes that you want between backups

4. Click OK to close the window

Lesson summary

In this lesson you:

Created a project

Set the projection parameters

Added images to the project

Saved the project



Lesson 1.2 - Setting up aerial photograph projects In this lesson you will:

Create a project


Enter camera calibration information

Add the airphotos to the project

Collect fiducial marks

Save your project

This lesson describes how to prepare four airphotos as part of the Data Preparation

stage. To set up your airphotos, you require:

Aerial photographs with camera calibration data

Map projection information

Note Airphoto is short for aerial photograph. Aerial photograph,

in the broadest sense, means a photograph taken from an

airborne platform.

In this lesson, you deal with strip photographs. Strip

photography refers to a number of consecutive

overlapping photos taken along a flight line, usually at a

constant altitude.



Workflow for aerial photograph projects

Figure 14. Workflow for aerial photograph projects



Creating a project

OrthoEngine works on a project-by-project basis. Therefore, you need to open an

existing project or create a new project before you gain access to the functions within OrthoEngine.

In this lesson, you will set up a new project using four aerial photographs data.

To create a new project

1. On the OrthoEngine window in the File menu, click New

The Project Information window opens.

2. Click Browse


3. Locate the AIRPHOTO folder

4. In the File name box, enter airphoto.prj and click Save

The path and filename appear in the File name box in the Project Information

window.

5. In the Name box, enter Airphoto Project

6. In the Description box, enter Airphoto ortho project for Richmond Hill,

ON

7. For the Math Modelling Method, select Aerial Photography

Camera Type

Select the type of camera in the Options area of the Project Information window.

The options are:

Standard Aerial camera

Digital/Video camera

ADS

The photos used in this lesson were taken with a Standard Aerial camera.

Note If your photos were taken with a Digital/Video camera,

refer to the Digital Airphoto exercise in the OrthoEngine

Workbook for further details.

To select a camera type

8. In the Camera Type section, select Standard Aerial

Exterior Orientation

The exterior orientation is:

Computed from ground control points and tie points, or

Provided by the user

Many aircraft are equipped with onboard Global Positioning Systems (GPS), and

sometimes with Inertial Navigation Systems (INS). These systems collect the

exterior orientation of the camera directly on the aircraft. Select User Input to use

the GPS and INS readings alone and accept them as correct. Select Compute from

GCPs and Tie Points to use ground control points and/or tie points to refine the GPS and INS results.

To select an exterior orientation



9. In the Exterior Orientation section, select Compute from GCPs & tie

points

10. Click OK

The Project Information window closes and the Set Projection window opens.

Figure 15. Project Information window after the information is entered

Note If you wish to modify your project information at any

time, reopen the Project Information window. However,

you cannot change the math model once it has been set.

Figure 16. Set Projection window

Setting the projection parameters

A projection is a method of portraying all or part of the earth on a flat surface.

Output Projection defines the final projection for orthoimages, mosaics, 3-D features, and digital elevation models (DEMs).

GCP Projection defines the projection of your source of ground control information

used during either manual ground control point (GCP) collection or when importing

GCPs from text file. If you collect GCPs from a geocoded source, the coordinates

are reprojected to the GCP Projection and saved into the project file.



If you collect GCPs from multiple sources, you can change the GCP Projection to

match each source using the Set Projection window. Using different projections

increases processing time during orthorectification, but it means that you do not have to reproject your ground control prior to using it in OrthoEngine.

The projection information needs to be set at the beginning of each project. In the

Set Projection window, enter the projection information for the Richmond Hill,

Ontario area.

Output Projection

To enter the Output Projection parameters

1. From the list to the left of the Earth Model button, select UTM

The Earth Models window opens.

2. On the Datums tab

3. Select D000 and click Accept

The UTM Zones window opens.

4. Select Zone 17 and click Accept

The UTM Rows window opens.

5. Select Row T and click Accept

6. In the Output pixel spacing box, type 0.4

7. In the Output line spacing box, type 0.4

GCP Projection

To enter the GCP Projection parameters

8. Under GCP Projection, click Set GCP Projection based on Output

Projection.

The GCP Projection adopts the same settings used for the Output Projection.

9. Click OK.

The Set Projection window closes and the Standard Aerial Camera Calibration

Information window opens.

Note If you wish to modify the projection information, reopen

the Set Projection window.

Changes to the projection mid-project will make any

existing orthophotos invalid.

Entering the camera calibration data

The camera calibration data is used to identify and correct the distortions

introduced into the image due to the curvature of the lens, the focal length, and the

perspective effects. This information is used to compute the interior orientation, which is the relationship between the film and the camera.

Images taken with a standard photogrammetric aerial camera usually come with a

report that provides data about the camera.

Focal Length

The Focal Length is the distance between the focal point of the lens and the film.

Entering an incorrect focal length may introduce unwanted distortions in your project. This is a compulsory parameter.



Radial Lens Distortion

Radial Lens Distortion is the symmetric distortion caused by the lens due to

imperfections in curvature when the lens was ground. In most cases, the errors

introduced by radial lens distortion (around 1 to 2 um) are much smaller than the

scanning resolution of the image (around 25um). Entering the values may

significantly increase the processing time while contributing very little value to the

final product. The values for the Radial Lens Distortion may be provided to you as

R0 through R7 coefficients or in tabular format. These parameters are optional and the coefficients may or may not appear in the camera calibration report.

If you are using a USGS camera calibration report, the coefficients are given as K0,

K1, K2, K3 and K4, which correspond to R1, R3, R5, and R7. K4 is discarded since it is usually zero.

Fiducial Marks

Fiducial marks are small crosses or small V-shaped indents located precisely on

each of the four corners and/or exactly midway along the four sides of a standard

aerial photograph. After you identify the fiducial marks in your scanned image,

OrthoEngine uses the fiducial marks entered from the camera calibration report to

establish an image coordinate frame. The fiducial mark coordinates are a compulsory parameter for standard aerial photographs.

Photo Scale

Image Scale is the ratio of the size of the objects in the image to the size of the

objects on the ground. This parameter is optional, except when you want to import GPS/INS observations and use them during the automatic tie point measurements.

Entering the incorrect Image Scale may cause the computation of the math model

(the bundle adjustment) to fail.

Earth Radius

The Earth Radius is the radius of curvature of the earth at the location of the

images in the project. This parameter is optional since aerial photographs usually

use a large scale (for example, 1:8,000) and the error due to the earth's radius is

negligible. You only need earth radius correction for images with a scale over 1:20,000.

To enter the camera calibration data

1. In the Standard Aerial Camera Calibration Information window, enter

the information shown in Table 2.

Camera Calibration Data

Focal length 152.856

Position Corner

Top Left -106.000 106.000

Top Right 106.000 106.000

Bottom Right 105.996 -106.000

Bottom Left -105.996 -106.000

Image Scale 1:8000



2. After all the data is entered, click OK

Figure 17. Standard Aerial Camera Calibration Information window

Adding the airphotos

This section describes how to add the airphotos to the project file. The project file

will then contain the filename and location of each input photo.

To import images into the project

1. On the OrthoEngine window in the Processing Step list, select Data Input.

A new toolbar with five icons appears on the window.

Figure 18. Data Input toolbar



2. On the Data Input toolbar, click Open a new or existing image


3. Click Add Image


4. Locate the AIRPHOTO folder

5. Press the CTRL key and select files S129.pix, S130.pix, S188.pix and

S189.pix and click Open

The Multiple File selection message window opens indicating the total number

of files that are detected and are to be loaded into the project.

6. Click OK

The four photos are listed in the Open Image window.

To open the first photo

1. In the Open Image window, select S129.pix and click Open

A viewer opens displaying photo S129.pix. In addition, the Fiducial Mark

Collection window opens for this photo.

Figure 19. Viewer showing photo S129.pix



Figure 20. Fiducial Mark Collection window

Collecting fiducial marks

OrthoEngine links the fiducial mark coordinates entered from the camera calibration

report to the positions that you identify on the scanned image. You must identify the fiducial marks in every image.

Note If you are working in a project with a large volume of

images, it is recommended that you enter the fiducial marks and ground control points for a limited number

of images (up to five), complete the calculation of the math model, and then check for errors before continuing. It is easier to locate bad points on a few

images than over the entire project.

Manual collection

To manually collect fiducial marks

1. Click the approximate location of the fiducial mark in the top left corner, using

the zoom tools as necessary

A red crosshair appears in the viewer.

2. Click precisely in the center of the fiducial mark

3. In the Fiducial Mark Collection window, click Set beside the Top left pixel and

line boxes.

The Pixel and Line coordinates for the fiducial mark appear in the window.

4. Repeat steps 1 to 3 to collect fiducial marks in the Top right, Bottom right

and Bottom left corners

5. For the Calibration Edge, select Left

This is the position of the data strip as it appears in the image on the screen.



Errors

Under Errors, OrthoEngine compares the computed fiducial mark positions based on

the measurements taken from the screen with the fiducial information that you

entered from the camera calibration report. Click Clear beside any fiducial marks where the error is not acceptable and repeat the collection process.

The error should be less than one pixel, unless the image is scanned at a very high

resolution. Large errors may indicate that either the coordinates from the camera

calibration report were entered incorrectly or the fiducial mark was collected

incorrectly from the scanned image.

Automatic collection

After collecting the fiducial marks manually for one of your images, OrthoEngine

can use automated pattern matching to automatically collect the fiducial marks for the rest of your images in the project.

To automatically collect fiducial marks for the remaining photos

1. After manually collecting fiducials for the first photo, click Auto Fiducial

Collection

2. The Auto Fiducial Collection window will open. Click Run

3. After the Progress Monitor closes, click OK

4. To accept the fiducial marks, click OK

The Fiducial Mark Collection window closes.

You can verify the accuracy of the fiducial mark collection by viewing the fiducial.rpt report in the folder where the project is saved.

Note This is a good time to save your project file.

Lesson summary

In this lesson you:

Created a project


Entered camera calibration information

Added the airphotos to the project

Collected fiducial marks

Saved your project

Checkpoint

You are now ready to proceed to Module 2: Computing the math model. In Module 2,

you collect ground control points and tie points for your project, and then calculate the math model by way of a bundle adjustment.


Module 2: Computing the math model

About this module

Module 2 has three lessons:

Lesson 2.1 - Collecting ground control points

Lesson 2.2 - Collecting tie points

Lesson 2.3 - Calculating the math model

Module 2: Computing the math model Geomatica OrthoEngine


Checking the aerial photography project workflow

Figure 21. Aerial photography project workflow

Geomatica OrthoEngine Module 2: Computing the math model


Lesson 2.1 - Collecting ground control points In this lesson you will:

Collect GCPs from a geocoded image

Import GCPs from a text file

Collect stereo GCPs

To add GCPs, you require:

The airphoto.prj project file from Module 1 for the aerial photographs

S129.pix, S130.pix, S188.px and S189.pix.

The air_mos.pix mosaic file contained in the AIRPHOTO folder, which serves

as the georeferenced image.

Note A ground control point (GCP) is a feature that you can

clearly identify in the raw image for which you have a

known ground coordinate.

Ground coordinates can come from a variety of sources such as the Global

Positioning System (GPS), ground surveys, geocoded images, vectors, Geographic

Information Systems (GIS), topographic maps, chip databases, or by using

photogrammetric processes to extend the number of GCPs in your images. A GCP

determines the relationship between the raw image and the ground by associating

the pixel (P) and line (L) image coordinates to the x, y, and z coordinates on the ground.

Although the media, formats, and methods used to collect the coordinates are

different depending on the source, the idea is the same. You have to match a point in the raw image to a set of coordinates.

Since some sources of ground control only offer dispersed points, it may be more

efficient to select a point in the source first and then locate it in the raw image. For

example, a vector file may have a limited number of features available as ground control compared with a geocoded image.

Collecting good GCPs

Select features that are close to the ground. Because elevated features in the

image will appear to “lean”, selecting features on the ground will ensure that the point is not displaced from the actual ground coordinate.

Avoid picking shadows. These are easy to see in the image, but they are not

permanent features and can move from one image to another.

Avoid repetitive features such as parking lots and lines on a highway, since it

is easy to select the wrong one.

When collecting GCP coordinates in the field (via GPS or survey), try to

identify good targets in the raw image before arbitrarily collecting coordinates in the field.

GCPs should be collected in a wide distribution over the image and the

project. Ensure that the GCPs are collected from a variety of ground elevations.

A GCP may be selected on a single image, or may be selected in an area of

overlap between 2 or more images. GCPs selected in multiple images help to produce a more accurate model.



How many GCPs?

The minimum number of GCPs you need to collect depends on the type of data you

are correcting, the processing level of that data, and which math model you are using. For more information, please refer to Appendix A.

For the Aerial Photography model, the minimum requirement is that you have at

least two GCPs on at least one photo in the project, to ensure scale. However, there

should be a few photos with 3 GCPs in the project. This provides correct levelling and scale for the math model. Tie points can hold the rest of the project together.

Note If you are working in a project with a large volume of

images, it is recommended that you enter the ground

control points for a limited number of images (up to five),

complete the calculation of the math model, and then

check for errors before continuing. It is easier to locate

bad points on a few images than over the entire project.

Collecting ground control points

If you have several images open, one image resides in a viewer labeled Working

while the others are labeled Reference. The GCP Collection window collects and

displays the GCPs from the image in the Working viewer only. Click the Reference button to switch the viewer to Working.

You will now open S129.pix as the Working Image.

To open the Working Image

1. On the OrthoEngine window in the Processing step list, select GCP/TP

Collection

A new toolbar with seven icons appears. The icons on the toolbar are

shortcuts to all the functions you need during GCP and TP collection.

Figure 22. GCP/TP Collection toolbar

2. On the GCP/TP Collection toolbar, click Open a new or existing image

The Open Image window opens listing the four photos in this project.

3. Select S129.pix and click Open

A viewer opens containing the S129.pix photo as the Working Image.



Figure 23. Viewer containing S129 as the Working Image

Collecting GCPs from a geocoded image

Before you begin collecting ground control points on the Working Image, you need

to load the geocoded image.

To load the geocoded image

1. On the GCP/TP Collection toolbar, click Collect GCPs Manually

The GCP Collection for S129 window opens.



Figure 24. GCP Collection window

2. From the Ground control source list, select Geocoded image

A File Selector window opens automatically.

3. From the AIRPHOTO folder, select air_mos.pix and click Open

The air_mos.pix file is loaded in a viewer as the Geocoded Image and is listed

at the top of the GCP Collection window.

4. Make sure the Auto locate and Compute model boxes are checked

If you chose the Aerial Photography, Satellite Orbital, Rational Functions, or Thin

Plate Spline math models, you can use a digital elevation model (DEM) to

determine the elevation of your GCPs. The DEM does not have to be in the same projection as the source of the GCPs.

To load the DEM to set elevation

1. Beside the DEM box, click Browse

2. From the AIRPHOTO folder, select AP_DEM.pix and click Open

The DEM File window opens where you select the channel containing the DEM

information. This will be your source of elevation for your ground control

points.

3. Enter a Background elevation of -150 and click OK



Background elevation

Background elevation represents those areas inside the DEM for which there is no

data provided. For DEMs generated by OrthoEngine, the background elevation

defaults to -150. Other DEMs have different background elevation values that you must know before they can be used.

Note If you do not know the background value, click DEM Info

in the DEM File window. The window displays the three

lowest and three highest values in the DEM.

To collect GCPs from a geocoded image

1. In the air_mos.pix viewer, place the crosshair near the left edge of the image

at the position shown in the figure below

Figure 25. Location of first GCP (circled in red)

2. Place the cursor on the location shown in the figure below, zooming in as

necessary



Figure 26. Location of crosshair for G0001 (circled in red)

3. In the Geocoded Image viewer, click Use Point

The georeferenced coordinates for this location are transferred to the GCP

Collection window. They should be approximately:

627260 X

4857514 Y

4. In the GCP Collection window, click Extract Elevation

5. Place the crosshair on the same feature in the uncorrected S129.pix photo

6. When you are satisfied with the position of the crosshair, click Use Point

The image coordinates for G0001 are transferred to the GCP Collection

window. They should be approximately:

320 Pixel

2257 Line

7. In the GCP Collection window, click Accept

The GCP information is transferred to the Accepted Points list for the GCP with Point ID G0001.



Note The Point ID is a label automatically assigned to each

GCP. You can type a new label in the Point ID box,

however, all points (ground control points, independent

check points, tie points, and elevation match points) in

the image must have unique labels. When collecting

stereo GCPs (the same GCP in the overlap areas of

different images), use the same Point ID in each image.

The same workflow is used when collecting GCPs from

geocoded vectors.

You can edit the error estimate in the +/- boxes to

correspond to your ability to precisely identify a feature in

the image. For example, if you use coarse imagery, you

can probably only measure to the closest pixel. If you use

imagery that was compressed or poorly scanned, you may

only be able to measure to the closest two pixels. Even if

you identify a GCP to the closest pixel, the coordinate

may only be accurate to a certain number of meters.

To collect the second GCP

1. Follow steps 1 to 7 above to collect a GCP at the location shown in Figure 27, below.

Figure 27. Location of G0002 on air_mos.pix

To collect the third GCP

1. Follow steps 1 to 7 above to collect a GCP at the location shown in the figure below



Figure 28. Location of G0003 on air_mos.pix

Collecting GCPs from a PIX/text file

Ground control points collected with a GPS will often be delivered in a text file. Each

point will have X, Y, E and possibly Point Id information. Before you collect ground

control points in the field, you need to ensure that you can clearly see that location

in the raw image. The pixel and line coordinates for the uncorrected image must be

determined manually and transferred to the GCP Collection window.

To import GCPs from a file

1. From the Ground control source list in the GCP Collection window, select

PIX/Text file

The Read GCP From PIX/Text File window opens.

2. Click Select

3. From the AIRPHOTO folder, select S129.GCP and click Open

4. From the Sample formats list, select ID X Y Z

ID X Y Z format

The format of the S129.GCP file is ID X Y Z, which means that each row of text contains the following information from left to right:

The GCP ID (ID)

The georeferenced East/West coordinate (X)

The georeferenced North/South coordinate (Y)

The elevation (Z)

The Pixel and Line positions will be taken from the uncorrected S129 photo.



To apply the format

1. Click Apply Format

The information for each point is listed in the GCPs extracted from file area.

Figure 29. Read GCP From Text File window with GCPs in ID X Y Z format

2. Click OK

The GCP Text file window opens.

Figure 30. GCP Text File window



To transfer the coordinates

1. In the GCP Text File window, select G0014 and click Transfer to GCP

collection panel

The ID X Y Z coordinate is transferred to the GCP Collection window. If Auto

locate is enabled in the GCP Collection window, OrthoEngine will estimate the

position of the GCP in the uncorrected image. In the viewer for S129, you will

notice that the crosshair have automatically moved near the correct position

for G0014.

2. Use the figure below to more accurately position the crosshair for G0014

Figure 31. G0014 on S129.pix

3. Adjust the position of the crosshair and click Use Point



2665 Pixel

2392 Line


The GCP information is transferred to the Accepted Points list for the GCP with

Point ID G0014.

5. In the GCP Text File window, select G0015 and click Transfer to GCP

collection panel

6. Use the figure below to more accurately position the crosshair for G0015



Figure 32. G0015 on S129.pix

7. Adjust the position of the crosshair and click Use Point



4131 Pixel

4159 Line


The GCP information is transferred to the Accepted Points list for the GCP with

Point ID G0015.


To save time, the GCPs for S130.pix, S188.pix and S189.pix will be imported from a text file in ID P L X Y Z format.

IPLXYZ format

The format of the S130.GCP file is ID X Y Z, which means that each row of text

contains the following information from left to right:

The GCP ID (I)

The image pixel position (P)

The image line position (L)

The georeferenced East/West coordinate (X)

The georeferenced North/South coordinate (Y)

The elevation (Z)

To open S130.pix as the Working Image



1. On the GCP/TP Collection toolbar, click Open a new or existing image


2. Select S130 and click Open

The Open Image window closes and a viewer opens containing the S130

photo as the Working Image.

Note One image is always the Working image, while the other

image is the Reference image. Click Reference on the

viewer toolbar to set an image to Working.

To import the GCPs for S130.pix

1. On the GCP Collection window, click Select PIX/Text File

The Read GCP from Text File window opens.

2. Click Select

3. From the AIRPHOTO folder, select S130.GCP and click Open

4. From the Available formats list, select ID P L X Y Z

5. Click Apply Format

The information for each point is listed in the GCPs extracted from file area.

Figure 33. Read GCP From Text File window for GCPs in ID P L X Y Z format

6. Check that the GCPs are listed correctly and click OK

After the GCPs are read in, the GCP ID number for each point appears in the

viewer in red. Check to see that the GCP positions are correct.

To import the GCPs for S188.pix and S189.pix:

1. Open S188.pix and S189.pix

2. Make S188 the Working image

3. Import the GCPs for S188 using the S188.GCP text file

4. Make S189 the Working image



5. Import the GCPs for S189 using the S189.GCP text file

Note When a photo is selected as the Working image, the GCPs

collected for this image are listed in the GCP Collection

window.

Collecting stereo GCPs

A stereo ground control point (GCP) is a cross between a regular GCP and a tie

point. It is a feature with known ground coordinates that you can clearly identify in

two or more images. They have the same Point ID, Easting and Northing

coordinates and elevation value, but the pixel and line location is different in each image.

Therefore, a stereo GCP not only determines the relationship between the raw

images and the ground, like a GCP, but also identifies how the images in your

project relate to each other, like a tie point. The result is a stronger math model

since the stereo GCPs add redundancy and are weighted more heavily in the

calculation of the math model.

To collect Stereo GCPs

1. Ensure photos S129 and S130 are open, as well as the GCP Collection window

2. Make S130.pix the Working image

3. In the GCP Collection window, locate and select Point ID G0016

This loads the GCP image and georeferenced position information based on

the S130.pix photo. If Auto Locate is selected, this GCP is loaded in each of

the viewing windows.

4. Make the S129.pix photo the Working image by clicking Reference on the

viewer toolbar

A list of GCPs collected for the Working image now appears in the GCP

Collection window. The identification of the desired stereo point, G0016, is

listed in the GCP Collection window for S129. The georeferenced position

information for this point is shown in the window. However, the image pixel

and line information for the Working image is not listed, since you have not

yet associated the point G0016 with S129.pix.

5. Place your cursor on the feature that corresponds with point G0016 on S129



Figure 34. G0016

6. Click Use Point

Note that the Image pixel and line information updates in the GCP Collection

window.

7. Click Accept

This point is now registered to the same georeferenced location on the earth

for both overlapping images, making it a Stereo GCP.

8. Collect another stereo GCP based on Point ID G0003

Figure 35. G0003

Check points

Whereas GCPs are used in computing the math model, Check Points are used to

check the accuracy of the math model and are not used to compute the math

model. OrthoEngine calculates the difference between their position and the

position determined by the model; therefore, the Check Points provide an

independent accuracy assessment of the math model. Typically Check Points should

be used for points that are known to highly accurate. For instance, GPS surveyed

points make good Check Points. You can create a Check Point in the same way that

you create a GCP. And it is even possible to convert a GCP to a Check Point and vice versa.

Next, you will take an existing GCP and turn it into a Check Point.

To create a Check Point

1. Make S130.pix the Working image

2. In the GCP Collection window, select G0014 or a highly accurate GCP

3. From the list beside the Point ID box, select Check

4. Click Change

This point is now a Check Point and is not included in the bundle adjustment.

To convert a Check Point to a GCP



1. In the GCP Collection window, select G0014

5. From the list beside the Point ID box, select GCP

2. Click Change

This point is once again a GCP and is included in the bundle adjustment.

3. Click Close


Lesson summary

In this lesson you:

Collected GCPs from a geocoded image

Imported GCPs from a text file

Collected stereo GCPs



Lesson 2.2 - Collecting tie points In this lesson you will:

Collect tie points manually

Collect tie points automatically

Check the layout of the images

Collecting tie points

To add tie points, you require:

The project file airphoto.prj from Lesson 2.1 for photos S129.pix, S130.pix,

S188.pix and S189.pix.

The objective of this lesson is to tie the four photos together and check the layout

to ensure a proper distribution of points.

Note A tie point is a feature that you can clearly identify in two

or more images that you can select as a reference point.

Tie points do not have known ground coordinates, but you can use them to extend

ground control over areas where you do not have ground control points (GCPs).

Used in rigorous models such as Aerial Photography and Satellite Orbital (high and

low resolution) math models, tie points identify how the images in your project

relate to each other. In a project using the Rational Functions math model where

you have imported the polynomial coefficients distributed with the data, you can

collect tie points and ground control points to compute a transformation to improve the fit between the images.

For projects using the Aerial Photography math model, you usually collect tie points

in a three-by-three pattern over the image. Since the images have a 60 percent

overlap between each other and a 20 percent overlap between the strips, you can use the three-by-three pattern to connect six overlapping images.

Projects using the Satellite Orbital math model generally have fewer images so you

can collect tie points wherever overlap occurs. Since the overlap between satellite

images is unpredictable, satellite imagery generally covers a large area containing a lot of ground control.

Using the tie points in the calculation of the math model ensures the best fit not

only for the individual images, but for all the images united as a whole. Therefore,

the images will fit the ground coordinate system, and overlapping images will fit

each other.

Selecting good TPs

Select features that can be identified accurately at the resolution of the raw

image.

Select features that are close to the ground. Because elevated features in the

image will appear to “lean”, selecting features on the ground will ensure that the point is not displaced from the actual ground coordinate.

Avoid picking shadows. These are easy to see in the image, but they are not

permanent features and can move from one image to another.



Avoid repetitive features such as parking lots and lines on a highway, since it

is easy to select the wrong one.

While tie points that join two images together are effective, tie points that

join 3 or more together are even better. Tie points that join multiple images together produce a more accurate model.

If the elevation value at the tie point location is known, then enter that value

in the elevation field in the tie point collection panel. These points help to quantify elevation, improving the accuracy of the geometric model.

Collecting tie points manually

If you have several images open, you will notice that one image resides in a viewer

labeled Working while the others are labeled Reference. The Tie Point Collection

window collects and displays the tie points from the image in the Working viewer

only. Click the Reference button to switch the viewer to Working. You can collect

the same tie point in each image by clicking Reference in a viewer, collecting the tie

point, and then repeating the process for each image.

Notice that the block of photos have an area in common. This area represents the

overlap. The figure below depicts the four photos with the area of overlap bounded by a red rectangle.



Figure 36. Overlap

To collect tie points manually

1. On the GCP/TP Collection toolbar, click Manually collect tie points.

The Tie Point Collection window opens.



Figure 37. Tie Point Collection window

2. In the Bundle Adjustment Options section, click Select.

3. From the AIRPHOTO folder, select AP_DEM.pix and click Open.

The DEM File window opens where you select the channel containing the DEM

information. This will be your source of elevation for your tie points.

Entering tie point elevation is optional. You can either load a DEM or select

the Elevation option and manually enter the elevation. The elevation of the tie

point is automatically incorporated into the math model.

4. Enter a Background elevation of -150 and click OK.

5. Make S129.pix the Working image.

6. Find a location in S129.pix that can be clearly seen in the overlap area in

S130.pix, zooming in as necessary.

7. Click to place the cursor at this location.

A red crosshair appears.



8. In the S129 viewer, click Use Point.

If Auto locate is enabled in the Tie Point Collection window, OrthoEngine

estimates the position of the Tie Point in the overlap area of the other photos.

You need to refine the position of the crosshair on S130.pix before accepting

the tie point on this image.

9. Place the cursor at the exact same location in S130.pix, zooming in as

necessary.

10. In the S130 viewer, click Use Point.

11. In the Tie Point Collection window, click Accept.

The point is listed in the Accepted Tie Points table.

12. Repeat these steps to collect at least four additional tie points for the project.

For Auto locate to work, you need to click Use Point on the Working image

after placing the crosshair on a feature that can be seen in both images. It

does not matter which image you set as the Working image.

Tie points can also be collected between flight lines.

13. Click Close.


Collecting tie points automatically

Since tie points are simply matching points in two or more images, OrthoEngine

can automate the tie point collection by using image correlation techniques. Image

correlation uses a hierarchical approach to find matching features in the

overlapping area between two or more images using moving frame with a search

radius of 100 pixels by default.

The first attempt at correlation is performed on very coarse versions of the images.

Depending on the resolution of the images or the accuracy of the math model, the

predictability of the match can be greater than the size of the search frame. For

example, if your image is 0.10 meter resolution and your estimated math model is

off by 15 meters, then the features that you are trying to match are 150 pixels

away from their estimated locations.

Therefore, if you are experiencing a low success rate with the Automatic Tie Point

Collection, increasing the search radius may improve the results Increasing the

search radius, however, will increase the processing time.

The matching process can be accomplished if your project meets one or more of

the following criteria:

The exterior orientation of each image was computed based on ground control points (GCPs) and/or tie points.

You collected three tie points between every pair of overlapping images.

You used a Global Positioning System (GPS) to obtain the x, y, and z

coordinates for each image center, and you estimated the omega, phi, and

kappa rotations, or they were supplied by an Inertial Navigation System

(INS).

You used the Satellite Orbital math model and imported the ephemeris or

orbit information with the satellite data.

You imported the polynomial coefficients from the satellite data for use with

the Rational Functions math model.



Now that you have collected some tie points manually, you will collect tie points

automatically.

To open the Automatic Tie Point Collection window

1. On the GCP/TP Collection toolbar, click Automatically collect tie points.

The Automatic Tie Point Collection window opens.

Figure 38. Automatic Tie Point Collection window

Tie point distribution pattern

There are two options for the distribution pattern:

Entire image

To distribute the tie points evenly over the entire image and match each tie

point in all the overlapping images. This is normally used to generate

standard tie point distributions for aerial photographs such as the three-by-three pattern.

Overlap area

To distribute the points evenly only in the overlap area between any pair of

overlapping images. This is normally used for satellite images or for aerial photographs with less than 60% overlap.

Tie point options

The Tie Point Options section contains the following four options:

Tie points per area

You specify the number of Tie Points to generate per overlap area for each pair of images.

Matching threshold This is a minimum correlation score between points that will be considered a

successful match. This aid in controlling the quality of the automatically

collected tie points. The range is from 0 to 1, with a default value of 0.75. Increasing the threshold may reduce the number of tie points accepted.



Search radius

This is the number of pixels defining the radius of the search frame. If you are

experiencing a low success rate with the Automatic Tie Point Collection, increasing the search radius may improve the results.

Approximate Elevation

Entering an approximate elevation allows the matching algorithm to make a

better first estimate of the parallax between the images. This improves the success rate of the collection process.

Images to process

Tie Points are automatically collected in one of two ways:

All Images

To collect tie points for all images in the project.

Working Image

To collect tie points for the image designated as the working image. This option is available if there is currently a Working Image in the viewer.

Processing start time

Two options are available for the start time:

Start now

Begins the process after you click the Collect Tie Points button.

Start at (hh:mm)

To run the process overnight, select the Start at (hh:mm) option, which lets

you start the process at any time within the next 24 hours.

To set up and run Automatic Tie Point collection

1. For the Distribution Pattern, use the default setting of Entire image.

2. For the Tie points per area, enter the value 3.

3. For the Min. acceptance score, use the default value of 0.75.

4. For the Search radius, use the default value of 100 Pixels.

5. Under the Elevation Search Strategy set the Constant height at 200 meters.

6. For Image to Process, use the default option of All images.

7. Click Collect Tie Points.

A progress bar appears at the bottom of the window to monitor the status.

After the process is complete, a message box opens indicating the total

number of tie points found.

8. Click Close.

Verifying automatic tie points

Automatic tie points should always be verified to ensure that a given point was

collected over the same feature on your imagery. This is especially important if

there are clouds or snow in the imagery as the image correlation technique used for

the TP collection process sometimes fails in these regions. Automatically collected tie points are given an ID with the prefix A.

To verify the automatic tie points

1. On the GCP/TP Collection toolbar, click Manually collect tie points.

The Tie Point Collection window opens.



2. Open all four photos.

3. Activate the Auto Locate option on the Tie Point Collection window.

4. In the Tie Point Collection window, select a tie point with the prefix A.

The viewers update to display the imagery at 1:1 resolution centered on the

selected TP.

5. If you are not satisfied with the auto tie point, click Delete.

6. Verify the remaining automatic tie points and click Close.

Reviewing Project Overview and Raw Image Summary

Table

The Project Overview window is a quality control tool that reveals the relative

positioning of the image footprints and displays a plot of the distribution of the

ground control points (GCPs) and tie points for the entire project. When the panel is

opened, images in the project are represented by a collection of crosshairs. Each crosshair represents the approximate image center.

The top of the window contains a toolbar for panning, zooming, photo selection,

and launching the Raw Image Summary Table. The left side of the window has

two areas for controlling the appearance and display of information in the window.

By default, the image centers (crosshairs for all images in the project) but you have

the option to change this to display the image footprints. Below the Display Type

area is a section for controlling the information presented in the window; options

include viewing the current selected image(s), Ground Control Points (GCP s), Tie

Points (TPs), Check Points (CPs), and whether or not to display the image or Point IDs.

The Raw Image Summary Table is a quality control tool that provides

information about the photos in a project. This window is useful when there are

many photos in a project and you want to determine if there are a sufficient

number of GCPs/TPs in the project, or if each photo has the required minimum

number of GCPs/TPs, the RMS Error meets your requirements.

The top of the window contains a toolbar for clearing the selected rows (photos),

locking selected photos, displaying the overlapping photos, and launching the

Project Overview window. At the bottom of the window you will find the total

number of photos in the project, the total number of GCPs, TPs, CPs, the overall GCP RMS Error and the overall TP RMS Error.

To open the Project Overview window

1. On the GCP/TP Collection toolbar, click Display project overview.

The Project Overview window opens. The Overview area shows the center of

each image in the project. The top of the window points northward.

2. In the Overview area, click the + icon besides the image to select items to

view. The options available per image are the label, image center, footprint,

imagery, GCPs, CPs and TPs.

3. Check mark GCPs and TPs to view in the display window

The GCPs are displayed as red crosshairs, while the TPs are displayed as blue

crosshairs. If you are not satisfied with the distribution, edit your GCPs and tie

points.



4. Click on Display Raw Image Summary Table to view information about each

image.

5. Close the Project Overview window.

Figure 39. Project Overview window showing GCPs and TPs

Figure 40. Raw Image Summary Table




Lesson summary

In this lesson you:

Collected tie points manually

Collected tie points automatically

Checked the project overview



Lesson 2.3 - Calculating the math model In this lesson you will:

Perform the model calculation

Examine vector residual plots

Read the residual report

To calculate the math model, you require:

The project file airphoto.prj for photos S129.pix, S130.pix, S188.pix and

S189.pix.

Understanding rigorous math models

The computation of a rigorous math model is often referred to as a bundle

adjustment. The math model solution calculates the position and orientation of the

sensor—the aerial camera or satellite—at the time when the image was taken. Once

the position and orientation of the sensor is identified, it can be used to accurately

account for known distortions in the image. When the model is calculated, the

image is not manipulated. OrthoEngine simply calculates the position and orientation of the sensor at the time when the image was taken.

In the Aerial Photography math model, the geometry of the camera is described by

six independent parameters, called the elements of exterior orientation. The three-

dimensional coordinates x, y, and z of the exposure station in a ground coordinate

system identify the space position of the aerial camera. The z-coordinate is the

flying height above the datum, not above the ground. The angular orientation of the camera is described by three rotation angles: Omega, Phi, and Kappa.

In the Satellite Orbital math model, the position and orientation of the satellite is

described by a combination of several variables of the viewing geometry reflecting

the effects due to the platform position, velocity, sensor orientation, integration

time, and field of view. The ephemeris data will allow a bundle adjustment to be computed immediately with or without GCPs or tie points.

During the math model calculation, OrthoEngine uses ground control points (GCPs)

and tie points combined with the knowledge of the rigorous geometry of the sensor to calculate the best fit for all images in the project simultaneously.

For Aerial Photography projects, the model calculation can only be performed after

you collect the minimum number of ground control points and tie points. If you are

using data from the Global Positioning System (GPS) with or without Inertial

Navigation System (INS) data, the math model calculation can be performed

immediately. Due to the ephemeris data, the math model calculation for Satellite Orbital projects is performed immediately with or without GCPs or tie points.

You can add GCPs and tie points to refine the math model's solution. Not all the

GCPs in your project will have the same reliability. When the math model

calculation is performed, the GCPs, tie points, GPS data, and INS data will be

automatically weighted inversely to their estimated error. The most accurate GCPs

or tie points should affect the solution the most, and the least reliable should affect

the solution the least. Using many GCPs and tie points provides redundancy in the

observations so that a few bad points will not greatly affect your model, and the bad points will be easier to identify.



Once the sensor orientation is calculated, it is used to drive all the other processes

such as digital elevation model extraction and orthorectification. You must obtain an accurate math model solution before continuing with other processes.

Computing the model

There are two ways to calculate the math model:

You can do an update after each GCP or TP is collected

You can calculate the model after all GCPs and TPs are collected

The Compute model checkbox appears on the GCP Collection and the Tie Point

Collection window when you are using a rigorous model project. When you select

Compute model, OrthoEngine calculates the math model every time you add a

point to the project. This can help you determine whether the point that you collected is good enough for your project.

To continuously compute model

1. At the top of the GCP Collection or bottom of the Tie Point Collection window, select the Compute model check box.

Note When a project’s model is not up to date, various tables

will show values in red indicating the model needs

recomputing. By having Compute model checked on it

means the project will recompute immediately after each

edit so you’ll never see red in the tables.

The second way to calculate the math model is found on the Model Calculations

toolbar on the OrthoEngine window.

To compute the rigorous math model

1. On the OrthoEngine window in the Processing steps list, select Model

Calculations.

A new toolbar with one icon appears.

2. Click Compute model.

Note If the Compute model has been checked on either the

GCP or TP collection windows the Compute model option

will be disabled as it will already be up to date.

Troubleshooting the math model solution

Since determining the best possible solution for the math model is the foundation of

your project, it is important for you to know if your solution is good enough to

achieve the results you expect. If it is not, you must also know what to do to adjust the model.

The Residual Errors will help you determine if the solution is good enough for your

project. Residual errors are the difference between the coordinates that you

entered for the ground control points (GCPs) or tie points and where those points

are according to the computed math model. You can see the residual errors for the

image on the GCP Collection windows in the Residual column or you can generate a Residual Report for the entire project.



Residual errors do not necessarily reflect errors in the GCPs or tie points, but rather

the overall quality of the math model. In other words, residual errors are not

necessarily mistakes that need to be corrected. They may indicate bad points, but

generally, they simply indicate how well the computed math model fits the ground

control system.

Note In Rational Functions computed from GCPs, Polynomial,

and Thin Plate Spline projects, images are not connected

together with tie points. Therefore, the math model and

the resulting residual errors are calculated for each image

separately. If you selected the Thin Plate Spline math

model for your project, the residual errors will always

indicate zero. Use Check Points to check its accuracy.

Another way to verify the quality of the model is to collect Check Points. Check

Points are not used to compute the math model, but OrthoEngine calculates the

difference between their position and the position determined by the model and

includes the error in the Residual Errors report. Therefore, the Check Points provide

an independent accuracy assessment of the math model.

In most projects you should aim for the residual errors to be one pixel or less.

However, you should also consider how the resolution of the image, the accuracy of

your ground control source, and the compatibility between your ground control source and the images can affect the residual errors.

You may want to use a topographic map as a ground control source, however,

features on topographic maps may be shifted several meters for aesthetic

reasons. This limits the accuracy of the coordinates that you can obtain from

the map. Also, the detail visible on a 1:50,000 scale topographic map may

not be compatible with high resolution imagery. For example, if you choose a

road intersection in a topographic map as your coordinate, the same road

intersection in the aerial photograph may consist of several pixels. Therefore,

the residual error will likely be larger than a pixel.

An existing LANDSAT orthorectified image may make a convenient ground

control source for registering a new IKONOS image, but the resolution of the

LANDSAT image is 30 meters and the resolution of the IKONOS raw image is

1 meter. Therefore, even if you could pick the right pixel in the IKONOS

image, your GCP from the LANDSAT image is only accurate to 30 meters. You

cannot achieve accuracy of 2 to 4 meters unless your ground control source is equally accurate.

GCPs with high residual errors can be selected to be disregarded in the

computation by turning them into inactive points. Inactive points are not used

in computing the project’s math model. So, you could make some points

inactive, recompute the model and see the effect those points were having. If

points really have no value and are clearly erroneous then they should be

deleted. But making points inactive can be useful in determining if they have value or not.

At first glance, a residual error of 250 meters in ground distance may appear

too high. However, if your raw data has a resolution of 1000 meters, such as AVHRR, you have already achieved sub-pixel accuracy.



Identifying errors in the model

Although residual errors are not necessarily mistakes that need to be corrected,

they may indicate problems with the math model. The following conditions may help you to identify such problems.

Outliers

A ground control point (GCP) or tie point with a very high residual error compared

to the others in the Residual Errors report may indicate an error in the original GCP

coordinate, a typographical mistake, or an error in the position of the GCP or tie point on the raw image. These points are called outliers.

To correct an outlier

1. Verify that the feature you picked in the raw image corresponds to the one

from your source.

2. Verify that the typed ground coordinate matches the coordinate listed in your

source.

3. Confirm that the ground coordinate you collected in the raw image is

consistent with the coordinate you selected from the vector or the geocoded image.

4. Verify that the projection and datum for the ground coordinate are correct.

Poor math model solution

If the residual errors for all the GCPs and/or tie points in general are high, it may

indicate a poor model solution. Poor model solutions can be the result of inaccurate

GCPs, errors in the projection or datum, inadequate distribution of the ground

control, or insufficient ground control.

Residual errors are all zero

If all the residual errors for the GCPs and tie points read zero, it usually indicates

that you have collected only the minimum number of ground control points or

fewer. Collect more GCPs and tie points.

However, if you selected the Thin Plate Spline math model for your project, the

residual errors will always indicate zero. Use Check Points to check the accuracy for the Thin Plate Spline math model.

Systematic trends in residual errors

If you have high residual errors in one part of an image or project, it can indicate

that you need more ground control in the problem area, or it may indicate that you

have one or more bad points in the area that are skewing the math model. Some bad points are difficult to identify since some points may compensate for others.

Vector residual plots

You can view a visual representation of the residual errors by superimposing

vectors of the collected points and residual errors over the image. This allows for

visual analysis and quality assurance of the math model. Observing the patterns in the vectors can help you identify possible causes and solutions for the errors.

For example:



1. If you collected a point on the southeast sidewalk corner instead of the

northwest corner, the vector would point to the position calculated by the model.

2. If all the vectors are pointing in the same direction and magnitude, it may

indicate a possible datum shift.

3. If the vectors point in one direction on the east to west flight lines and point

in the opposite direction in the west to east flight lines, it means that you may have collected the fiducials incorrectly.

To open the Display Residuals window

1. On the OrthoEngine window, select Tools > Options. The Options window

opens.

2. Select the Residual Display tab.

Figure 41. Display Residuals window

Residuals list

The residuals can be plotted using one of three formats:

X,Y

to display the x-axis and y-axis residual errors for each point as separate vectors.

XY

to display the residual error for each point as one vector representing the

combined x-axis and y-axis residual errors.



Z (for stereo GCPs and stereo check points only) to display the residual error for the elevation of each point as one vector along the y-axis.

Coordinate system

There are two options for the axis on which the residual error will be displayed.

Ground

to display the residual error using Easting and Northing directions (ground

coordinate system).

Image

to display the residual error using the x and y axis directions (image coordinate system).

To open a photo

1. From the Processing step list, select GCP/TP Collection.

2. Click Open a new or existing image.

3. Select S129.pix and click Open.

4. On the GCP/TP Collection toolbar, click Collect GCPs Manually.

The GCP Collection window opens.

You will now plot the residuals on the photo you opened.

To plot the residuals on a photo

1. In the Image Viewer window, click the Residual Display icon.

2. Select Ground Control Points, Check Points and Tie Points from the drop-down

list.

To view the vector residuals

1. In the GCP Collection window, select G0001 from the Point ID list.

The viewer is updated to show this point at a resolution of 1:1. The vector

residuals are plotted as red lines.



Figure 42. X,Y Residual plot for G0001 on S129

2. Select “Tools | Options” from the main OrthoEngine Window

The Options panel appears

3. Click “Residual Display” in the tree list on the Options panel.

4. On the Residual Display area, change the Coordinate system to Ground and

click Apply.

The Residuals are displayed using the ground coordinate system instead of

the image coordinate system.

5. Examine how the residuals are plotted when you select the NE and H options

from the Residual Display area in the Options window.

Note By default, the magnification factor for the residual plots

is set to 5. The magnification factor exaggerates the

appearance of the residual error so it becomes easier to

observe systematic patterns. If the residual error is one

pixel and the Magnify value is five, the residual error is

displayed as five pixels long in the viewer.

To highlight points with high residual values

1. Change the Coordinate system back to Pixels.

2. For the Residuals option, select X,Y.

3. In the Highlight residual greater than box, enter 1 pixels.

4. Click Apply.

5. In the GCP Collection window, select a GCP that has a Res X or Res Y residual

greater than 1.

Residual errors over the 1 pixel threshold are highlighted by an increase in

the thickness of the vector lines.

6. Click OK.



Reading the Residual Report

The Residual Report helps you determine if the math model solution is good enough

for your project.

To open the Residual Errors window

1. On the GCP/TP Collection toolbar, select Residual report.

Figure 43. Residual Errors window

Residual units Defines whether residual errors are reported in Ground units or Image pixels.

Show points

Lets you select which types of points to display.

Show in

All images

To display all the images in the project.

Selected image

To display one image in the project, click an image in the table under Image ID

and click Selected image, or you can type the image's identification in the Selected image ID box and press ENTER.

Sort by



Residual

To order the residual errors from the highest to the lowest value.

Data snooping

To order the normalized residual errors from highest to lowest probability of error

that is not noise. Because the residuals are highly correlated to one another, a

blunder in one point may cause all points to have higher residuals. Because of

this, it may be difficult to isolate the source of the problem. Data snooping tries

to uncorrelate the errors in order to isolate the bad point by calculating this statistical value.

Automatic Point Selection

Allows the user to define which points to be selected based upon their residual values.

Residual errors

For each point in the project, the Residual Errors window lists the information shown in the

following table.

Residual errors

Heading Description

Point ID The point’s identification number

Res The combined residual error

Res X The point’s X-axis residual error

Res Y The point’s Y-axis residual error

Type Type of point, GCP or TP

Image ID Image to which the point belongs

Image X X-coordinate - # of pixels from left

Image Y Y-coordinate - # of pixels from top

Comp X Computer adjusted X-coordinate

Comp Y Computer adjusted Y-coordinate

Note If you make any edits to your model in the Residual

Errors window, make sure you recalculate the model by

clicking Compute Model.

This would be a good time to save your project file.



Lesson summary

In this lesson you:

Performed the model calculation

Examined vector residual plots

Read the residual report

Checkpoint

Need to extract or build a DEM (Module 3)

After the preparation stage, you may need to extract a Digital Elevation Model or

build a DEM from existing data. If so, proceed to the data extraction stage that begins with Module 3: DEM operations. Here, you will extract a DEM from the four

airphoto scenes. Keep in mind that the same procedures can also be used to

extract a DEM from any stereo image pair.

Already have a published DEM product (Module 4)

If you have an existing raster DEM to use for orthorectification, go directly to the correction stage, which begins with Module 4: Orthorectification.

In Module 4, you will set up, select your DEM, and then orthorectify your satellite

images or aerial photographs.


Module 3: DEM operations

About this module


Lesson 3.1 - Creating epipolar images

Lesson 3.2 - Extracting and geocoding the DEM

Lesson 3.3 – DEM Editing

Lesson 3.4 - Building and Merging DEMs

Module 3: DEM operations Geomatica OrthoEngine


Lesson 3.1 - Creating epipolar images In this lesson you will:

Set up for epipolar image creation

Create epipolar images in batch mode

The objective of this lesson is to set up and convert four airphoto scenes into their

epipolar projections. OrthoEngine is able to create epipolar images for multiple

stereopairs in batch mode. You are able to add your stereopairs to a list and process them in one step.

To complete this lesson you require:

The airphoto scenes S129.pix, S130.pix, S188.pix, and S189.pix.

The project file airphoto_model.prj, which contains the four airphotos from

the Richmond Hill dataset. This project contains all the required GCPs and tie points and an up-to-date model.

About Epipolar images

Epipolar images are stereo pairs that are reprojected so that the left and right

images have a common orientation, and matching features between the images

appear along a common x axis. Using epipolar images increases the speed of the correlation process and reduces the possibility of incorrect matches.

Figure 44. Comparing raw images to epipolar images

Epipolar images are used in extracting DEMs from stereo images.

Geomatica OrthoEngine Module 3: DEM operations






Creating epipolar images

To open the Create Epipolar Images window

1. Open the provided airphoto_model.prj project file in OrthoEngine

2. On the OrthoEngine window in the Processing steps list, select DEM From

Stereo.

A new toolbar with five icons appears.

Figure 46. DEM From Stereo toolbar

3. On the DEM From Stereo toolbar, click Create Epipolar Image.

The Generate Epipolar Images window opens.



Figure 47. Create Epipolar Image window

You need to select the scenes to be used for the generation of the epipolar images.

Left image

This area displays the candidates for the left-looking image of the stereo pair. All four airphotos are listed in the Left Image area when the window opens.

Right image

The Right Image area lists the candidates for the right-looking image.

Because this project contains airphotos, the idea of Left and Right Images is not

important for DEM extraction. You simply need to select the left and right images in

order to create pairs of epipolar images. If you decide to proceed to 3-D feature

extraction, which also uses epipolar pairs, you will save time if you generate pairs

that can be use in 3-D viewing. If your photos are scanned north up, the photo that

is geographically on the left is the left image, and the photo that is geographically on the right is the right image.



To create the epipolar images

1. From the Epipolar Selection list, select Optimum pairs.

2. Click Add Epipolar Pairs To Table.

The List of Epipolar Pairs lists two sets of epipolar pairs: S129 with S130 and

S188 with S189.

Figure 48. Generate epipolar images

3. Select a Downsample factor of 2.

This is the number of image pixels and lines that will be used to calculate one

epipolar image pixel. For example, typing 2 means that two adjoining pixels

and two adjoining lines will form one pixel in the epipolar image. The spatial

detail of your resulting epipolar images will not be as high as your original

imagery. You can adjust this value if you see noisy features in your image

that you do not want to see in your DEM.



4. Click Save Setup.

Save Setup saves the options chosen for batch processing with Automatic

DEM Extraction.

Generate Pairs begins the process based on the time set under Processing

Start Time. Use this option if you are using the epipolar pairs for 3-D Feature

Extraction. If you are using the epipolar pairs for Automatic DEM Extraction,

you can either use this option or Save Setup.

5. Click Close.

Note This would be a good time to save your project file.

Now that you have setup to generate the epipolar pairs, you will set up for DEM

extraction in the next lesson. The epipolar pairs and the DEM will be generated all at once.

Lesson summary

In this lesson you:

Set up for epipolar image creation

Created epipolar images in batch mode



Lesson 3.2 - Extracting and geocoding the DEM In this lesson you will:

Set up to extract and geocode a DEM

Extract and geocode the DEM

About this lesson

This lesson describes how to extract, geocode, and edit a DEM. To complete this

lesson you require the airphoto epipolar pairs generated in the previous lesson.

To complete this lesson you require:

The airphoto scenes S129.pix, S130.pix, S188.pix, and S189.pix.


the Richmond Hill dataset. This project contains all the required GCPs and tie points and an up-to-date model.

A digital elevation model (DEM) is a digital file of terrain elevations for ground

positions. It is a raster layer representing the elevation of the ground and objects, such as buildings and trees, with pixel values in the images.

You can extract a digital elevation model (DEM) from stereo pairs of images, which

are two or more images of the same area taken from different viewpoints. This

method can be very useful for creating a DEM for inaccessible areas. You can obtain

stereo pairs from aerial photographs, digital or video images, and these sensors: ASAR, ASTER, IRS, IKONOS, SPOT, QUICKBIRD, RADARSAT and WorldView-1.

OrthoEngine uses image correlation to extract matching pixels in the two images

and then uses the sensor geometry from the computed math model to calculate x, y, and z positions.

Figure 49. Creating a DEM from stereo images



Setting up to extract and geocode a DEM

The process of extracting a digital elevation model (DEM) from stereo images

consists of three steps:

Convert the raw images into epipolar pairs.

Epipolar images are stereo pairs that are reprojected so that the left and right

images have a common orientation, and matching features between the images appear along a common x axis.

Extract DEMs from the overlap between the epipolar pairs.

The resulting DEMs are called epipolar DEMs. They are not georeferenced at this stage.

Geocode the epipolar DEMs and stitch them together to form one DEM.

The result is one DEM reprojected to the ground coordinate system.

Editing the epipolar DEMs

If you want to edit the DEM before it is geocoded, do not select Create Geocoded

DEM. The DEM extraction will produce a file that contains the epipolar pair in the

first channel, the correlation score (if selected) in the second channel, and the corresponding epipolar DEM in the third channel.

After you generate the epipolar DEMs, you can edit them, geocode them, and then

integrate them into one DEM.

Editing the geocoded DEM

When you use the Automatic DEM Extraction window to complete the entire process

in one operation, OrthoEngine builds a model based on all the selected epipolar

pairs and uses that model when the DEMs are geocoded. The geocoded DEMs are

automatically stitched together and saved in a file. Because OrthoEngine uses a

model to process all the epipolar pairs, the resulting integrated geocoded DEM is

slightly more accurate than if you completed the process manually.

You can edit the geocoded DEM, however, the file will not include the epipolar

pairs. If you selected Create Score Channel, the correlation score is saved as the first channel in the file and the geocoded DEM as the second.

To open the Automatic DEM Extraction window

1. Open the provided airphoto_model.prj project file in OrthoEngine

2. On the DEM From Stereo toolbar, click Extract DEM automatically.

The Automatic DEM Extraction window opens



Figure 50. Automatic DEM Extraction window

Stereo pair selection

The epipolar pairs you set up in the last lesson are listed in the Stereo Pair

Selection table.

To select the stereo pairs

1. In the Select column, click to select both stereo pairs. You can also click Select All to select all pairs that appear in the list.

If the epipolar pairs do not exist or are not available, OrthoEngine will automatically

generate the epipolar pairs using the options that you saved in the Generate Epipolar Images window.

Epipolar DEMs

The Epipolar DEM column specifies the output name for the extracted DEMs. Two

epipolar DEMs will be created from the two sets of epipolar pairs.

DEM report

A text report is generated during the DEM extraction process. The report indicates

the parameters used to extract the DEM as well as the correlation success. The names of the output DEM reports are specified in the DEM Report column.



Extraction options

The Extraction Options section contains the following options that govern the quality

and resolution of the DEM extraction:

Extraction method

The extraction method selected will impact the accuracy of the resulting

epipolar DEMs. The two methods available to extract the DEM are:

NCC (Normalized cross-correlation) - produces lower-quality results with more errors

and less detail, but with minimal processing time.

SGM (Semi-global matching) produces higher-quality results with fewer errors and

higher detail, but processing time is increased greatly.

Output DEM vertical datum

When elevation values are extracted from a stereo pair, they are normally

extracted at the ellipsoid height as defined by the output projection. To have these valued converted to Mean Sea Level (MSL) heights select MSL.

Pixel sampling interval

The Pixel Sampling Interval sets the frequency of samples taken in the processing

of the DEM extraction. It also controls the size of the pixel in the final DEM relative

to the input images. The higher the number you choose, the larger the DEM pixel will be, and the faster the DEM is processed.

Smoothing filter

The option to apply a range of smoothing filters to the DEM.

Delete Epipolar Pairs after use

This option deletes the epipolar pairs from the disk to save space after the DEM is generated.

Elevation range

The minimum and maximum elevations are generated automatically (default) for

each epipolar pair. To apply minimum and maximum elevations globally for all epipolar pairs select explicit.

The minimum and maximum are used to estimate the search area for the

correlation. This increases the speed of the correlation and reduces errors.

Failure value

This value is assigned to failed pixels within the extracted DEM. Specifying a value

assists the manual editing process. The default Failure Value is -100.

Background value

This value is used to represent “No Data” pixels in the DEM. The "No Data" or

background identifies the pixels that lie outside the extracted DEM overlap area so

they are not mistaken for elevation values. For DEMs generated by OrthoEngine,

the background elevation defaults to -150.



DEM detail

DEM Detail determines how precisely you want to represent the terrain in the DEM.

Selecting Extra High, High, Medium or Low determines at which point in the

correlation process you want to stop. Low means that the process stops during the

coarse correlation phase on aggregated pixels so the level of detail in the DEM will

be quite low. High means the process continues until correlation is performed on

images at full resolution. Selecting Extra-high goes beyond full-resolution using a smaller window to extract higher level details.

Terrain type

The terrain type is used to aid the algorithm in the matching process. A flat terrain

is a gentle, rolling terrain type. A Hilly terrain (the default) is an uneven surface

with varying heights but not mountainous. A Mountainous terrain is a surface with significant changes in elevations.

Output DEM channel type

This option allows you to save the DEM in either a 16-bit signed channel or 32-bit

real channel.

Apply Wallis filter

The Wallis filter applies a locally adaptive, spatially varying, contrast stretch to the

epipolar images. It is intended to uniformly balance image detail in both bright and

dark areas without negatively impacting the other.

Use clip region

This option allows you to process only the area determined in the Define Clip Region window, which results in smaller DEMs and faster processing.

To set the extraction and geocoding options

1. For the Extraction method select SGM

2. For the Output DEM vertical datum, select Mean Sea Level

3. Set the Pixel sampling interval to 2.

Every second pixel is sampled and processing time is reduced.

4. Set the Smoothing filter to Low

5. Select the option to Create Geocoded DEM.

This will geocode and merge the extract epipolar DEMs in one step.

6. For the Output File name, enter GeocodedDEM.pix.

7. For the resolution, enter 0.8 X and 0.8 Y

8. For the DEM Bounds, select All Images.

This will use the extent of all the images in the Stereo pairs table as the

extents for the DEM.

9. For the Output Options, select Blending.

Output options

As the epipolar DEMs are extracted and geocoded, they are added to the geocoded

DEM file. When a new geocoded DEM is added to the file and it overlaps an existing

geocoded DEM, you must choose a method to determine which pixel value will be used. There are three methods:



Blending

Uses cutlines to determine a seamline with minimal elevation change between

epipolar pairs. This option blends 10 pixels using the average of the overlapping input values on either side of the cutline.

Use last value

To replace the pixel values in the overlap area in the existing geocoded DEM by the pixel values of the geocoded DEM being added to the file.

Average

To replace the pixel values in the overlap area by the average pixel values between

the existing geocoded DEM and the one being added to the file.

Highest Score

To replace the pixel values in the overlap area by the pixel value with the highest

correlation score between the existing geocoded DEM and the one being added to the file. This option is only useful if you select Create Score Channel.

Figure 51. Automatic DEM Extraction after the set-up is complete

To extract the DEM



1. Click Extract DEM.

A Progress Monitor opens which shows the status of the extraction process.

After the extraction is complete, the Progress Monitor closes. A message box

opens with the message DEM Extracted successfully.

2. Click Close.

Lesson summary

In this lesson you:

Set up to extract and geocode a DEM

Extracted and geocoded the DEM



Lesson 3.3 - DEM Editing

Digital elevation models (DEMs) may contain pixels with failed or incorrect values.

You can edit the DEM to smooth out any irregularities and create a more pleasing

DEM. For example, areas such as lakes often contain misleading elevation values so setting those areas to a constant value improves your model.

To open the DEM Editing window

1. From the DEM From Stereo toolbar, click Manually edit generated DEM. A File Selector window appears.

2. Select the extracted DSM and click Open

A DEM editing Channel Selector appears.

3. Select the Extracted DEM channel

A Focus window and the DEM Editing window opens with the extracted DEM added.

Figure 52. DEM Editing window



Figure 53. Focus window displaying the geocoded DEM

Quality Assessment Tools

DEM editing in OrthoEngine is accomplished by using the DEM editing component in

Geomatica (Focus and FLY!). In this lesson we will start by using Geomatica FLY!

and then the Full resolution ortho preview to analyze the DEM for areas that require editing.



Geomatica FLY!

“Geomatica FLY!” provides a different 3D viewing perspective that is not possible in

Focus. This different perspective aids in image interpretation that assists in the

DEM editing process. In this exercise, you will use “FLY!” to familiarize yourself with

the image data that is draped over the extracted un-edited DSM.

Using “FLY!” before you perform any DEM editing tasks allows you to focus on the

elements of the DEM that require special attention before you apply any filtering to

the DEM.

To launch FLY! for DEM interpretation

1. Launch Geomatica Focus

2. In the Focus “Files” treelist, right select “Add”

3. Browse to the “DEM-Editing” folder within the OrthoEngine\Airphoto training

folder

4. Select the “Geocoded_DEM.pix” file and click “Open”

The file appears in the Focus “Files” treelist.

Figure 54. DEM in Files Treelist

5. Expand the Rasters by clicking on the + symbol

6. Right click and select the 32R extracted DEM | View as grayscale

7. The image is loaded to the Maps treelist and canvas

8. Select the “Geocoded_DEM.pix” layer in the Maps Treelist

9. With the layer highlighted, select “Layer | DEM editing”

The grayscale image is replaced with a shaded-pseudo color image and the

DEM editing panel is launched.



Figure 55. Extracted DSM loaded in Focus

10. On the DEM editing window, click the FLY! button (7th from left) to launch FLY!

FLY! is launched and the aerial photographs are draped overtop the extracted

DSM.



Figure 56. Geomatica FLY! with aerial photos draped over DSM

To change the viewing direction in FLY!

1. Use the mouse left button mouse and click anywhere in the FLY! viewer

The view is automatically updated, centered on the cursor location when the

mouse button was clicked.

To fly over the draped imaged

2. Click the “Free flight” button on the main FLY! window (2nd from left)

To stop flying over the draped imaged

3. Click the “Free flight” button

To control the speed and flying altitude above the photos

4. Use the FLY! Control Panel (1st button on main FLY! window)

5. Increase or decrease elevation

6. Increase or decrease speed

Exercise:

Take notes of areas that may require special consideration (spikes, pits, bumpy roads etc).

Full Resolution Ortho Preview

Another Quality Assessment tool is the full resolution ortho preview window. This

tool will help you understand the imagery that is draped over the DEM. It provides

you with a view of the ortho-imagery that will be generated with the current edits

applied to a DEM. The full resolution ortho window also provides you with access to

all of the images that overlap that defined area.



To open the Full resolution Ortho Preview window

1. Click the Define Preview region button on the main DEM editing panel

2. Move your mouse over the Focus viewing canvas

3. With your mouse, drag out a rectangular box

The full resolution window is launched

A progress monitor appears indicating the status of the live orthorectification

process

Figure 57. Full resolution ortho preview window

Exercise:

Randomly open the full resolution ortho preview for different areas of the DEM and

take notes of any anomalies that require special consideration.



Removing Pits and Bumps - DEM editing

A DTM or DEM used for orthorectification typically contains terrain heights and has

surface heights removed. Doing so provides a more geographically accurate ortho-

image.

To remove a pit

1. Zoom to the Central-west area of the DEM as seen below

2. On the DEM editing window, Edit via Polygons tab click the Create

Polygon Layer button

A new vector layer is added to the Focus Maps treelist

3. Click the New Shapes dropdown button

4. Select Polygon

5. Digitize a new polygon around one of the pits in the road as seen in the figure

below.

6. Select Remove pits (flat) from the drop-down list on the DEM editing

window

7. Enter 85 (pixels) for the size and 5 for the gradient percent



NOTE: before entering a size in pixels it is always a good idea to use the

measure tool, to determine the size of an object.

8. Click Apply

To remove a bump

1. Using the same polygon digitized to remove the above pit, select Remove

bumps (flat) from the drop-down list.

2. Enter 25 (pixels) for the size and 5 for the gradient percent

NOTE: before entering a size in pixels it is always a good idea to use the

measure tool, to determine the size of an object.

3. Click Apply

Applying a terrain filter

A DTM or DEM used for orthorectification typically contains terrain heights and has

surface heights removed. Doing so provides a more geographically accurate ortho-

image.

To apply a terrain filter

1. Zoom to overview



2. Delete all existing polygons

3. Digitize a new polygon surrounding the DEM

4. Select Terrain filter (flat) from the drop-down list on the DEM editing

window

5. Enter 100 for the Size and 10 for the Gradient

6. Click Apply



Figure 58. Extracted DTM with terrain filtering applied

Exercise:

Use the Quality Control tools to evaluate if additional DEM editing is required before proceeding to the orthorectification lessons.

Lesson summary

In this lesson you:

Used FLY! to mark areas in the DSM that required editing

Used the full resolution update ortho-image to detect areas in the DSM

needing improvement.

Removed a pit in a road

Removed a bump on a road

Performed terrain filtering to remove surface objects (building heights and

trees)



Lesson 3.4 - Building and merging DEMs In this lesson you will:

Build a raster DEM from 3D point data

Merge 3 raster DEMs

Define the georeferencing of the output DEM

About this lesson

This lesson describes how to build a raster DEM from 3D point data. To complete this lesson you require:


the Richmond Hill dataset. This project contains all the required GCPs and

tie points and an up-to-date model.

The file 3D-Points.pix that contains 3D point information in a vector layer

OrthoEngine can calculate the elevations from vector layers to generate a raster

digital elevation model (DEM), which is saved as a PCIDSK file (.pix). OrthoEngine

uses raster DEMs to orthorectify images. If your elevation data is stored as vectors

such as contours, points, TIN, or even a text file containing coordinates, you can

convert them into a raster DEM as long as the vectors are in any of the supported formats.

Note You can combine vectors from different layers and files to

generate a DEM.

Vector layers can contain:

Points

A point is a single coordinate (x, y, and z).

Lines

A line is a start and end coordinate with points in between to define the shape.

Polygons

A polygon is a line with the same start and end coordinate forming an area with

numerous points along the line to define its size and shape.

Contours

A contour is a line formed by a set of points representing the same value of a

selected attribute. Contours are usually used to represent connecting points on the ground with the same elevation.

TIN

A Triangulated Irregular Network (TIN) is a digital model of adjoining triangles

formed from points selected on the terrain to represent an accurate model of the

surface. The TIN model can contain coordinates and other geographical data.







Setting up to build a DEM

To open the Import & Build DEM window

1. Open the provided airphoto_model.prj in OrthoEngine

2. On the OrthoEngine window in the Processing step list, select Import & Build

DEM.

A new toolbar with six icons appears. These are tools for importing a building

a DEM from a raster file, DEM from GCPs/Tie points/Elevation Match points,

DEM from vectors/points, DEM from contours, DEM from a TIN, and manually

edit the generated DEM.

Figure 60. Import & Build DEM toolbar

3. On the Import & Build DEM toolbar, click DEM from vector/points.

The Input Vector Layer Selection window opens.



Figure 61. Input Vector Layer Selection window

To import the vector file to generate the DEM

1. In the Input Vector Layer Selection window, click Select.

2. From the AIRPHOTO folder select 3D-Points.pix and click Open.

The available vector layers are listed.

3. Under Vector layer available, select 2 [VEC]: Points: 3D Points and click the

down arrow.

The selected layer appears under Vector layers to interpolate.

Note You can have vector layers containing vectors, points,

contours, or TIN from a variety of different files in the list

of vectors to be interpolated.

4. In the list of Vector layers to interpolate, select the <3D-Points.pix> 2

[VEC]: Points layer.

5. In the Data type list, select Points.

This is the type of vector contained in this layer.

6. For the Elevation source, select ELEVATION.

This is the attribute field that stores the elevation values.



Figure 62. Input Vector Layer Selection Window

7. Click OK.

The Input Vector Layer Selection window closes and the Define Output DEM

File window opens.



Figure 63. Define Output DEM File window

Defining the output DEM file

After importing the source for generating the digital elevation model (DEM), you

determine the parameters of the DEM output.

To define output DEM file

1. In the Output DEM box, enter 10mDEM.pix.

2. To generate a DEM that covers the area where elevation data exists, click

Elevation Source Area.

Mosaic Area will generate a DEM that covers the area defined by the Mosaic

Area

Image Extents will generate a DEM that covers the extents of all the images

in the project. This is useful when you want the DEM to cover the images

being orthorectified, but extrapolating beyond the elevation source area can

cause significant errors in your project.

3. Enter -32768 as the Background elevation

4. From the drop-down list, select Use bounds and resolution to use as the

determining factor for creating the file

5. Enter 10 for the X & Y Pixel Size

6. Click Generate DEM

The DEM Generation window opens

7. Choose Finite Difference and use the default values of 4 Iterations



Figure 64. Define Output DEM File window

The No. of Iteration is the maximum number of times that the DEM is

smoothed.

The output DEM opens in a viewer.

Figure 65. 10m DEM built from 3D Vector Points



Merging multiple DEMs

When extracting DEMs from imagery, it is not always possible to obtain a DEM

footprint identical to the footprint of the stereo imagery, unless there is 100%

overlap between the stereo images. OrthoEngine can merge multiple raster files

(DEMs) of varying image resolutions into one DEM file that can be used in orthorectification, thus preserving all of the photo or image data.

To Merge multiple DEM files

1. On the OrthoEngine window in the Processing step list, select Import & Build

DEM.

2. On the Import & Build DEM toolbar, click DEM from raster file.

The Input DEM File Selection window opens.

Figure 66. Input DEM File Selection window

3. In the Input DEM File Selection window, click Select.

4. From the AIRPHOTO folder select 10mDEM.pix, RH-12_GeoDEM.pix and

GeocodedDEM.pix and click Open.

The available raster DEM layers are listed.

5. Under DEM merge set candidates, select all 3 files and click the down

arrow.

The selected layer appears under Set of DEMs to merge.

6. In the Set of DEMs to merge area, click on the GeocodedDEM.pix file and

use the Move Up or Move Down button as necessary to put the file at the

bottom of the list.



7. Repeat step #6 above for the RH-12_GeoDEM.pix file to ensure it is in the

middle of the list.

Your window should look like the screen capture below.

Figure 67. Input DEM Layer Selection window

8. Enter -32768 as the input background elevation value

NOTE: the background elevation values for all 3 files should be -32768. If it is

not, then you need to model the files to make them all have the same

background elevation values before merging. OrthoEngine does not support

multiple background elevation values in a single file.

9. Select Cubic as the resampling method

10. Click the Yes radio button to Interpolate holes

11. Click the OK button

The Define Output DEM File window will be launched

12. Enter “-32768 .pix” for the Output DEM filename

13. Enter -32768 for the output background elevation

14. Review the output file georeferencing parameters and adjust if needed

15. Click the “Generate DEM” button

The DEMs will be merged. The results should appear similar to the figure

below. For illustration purposes, the DEM below does not have any edits

(results from Lesson 3.3 - DEM Editing) applied to the GeocodedDEM.pix file.



Figure 68. Merged DEMs

Lesson summary

In this lesson you:

Built a raster DEM from contour data

Defined the georeferencing of the output DEM

Checkpoint

Module 4



You can now proceed to Module 4: orthorectification. In this module, you will

remove distortions (lens, terrain etc.) and create a planimetric, geometrically corrected image with uniform scale via the orthorectification process.


Module 4: Orthorectification

About this module

Module 4 has one lesson:

Lesson 4.1 - Generating the orthorectified images

Lesson 4.2 - Adjusting orthorectified images

Module 4: Orthorectification Geomatica OrthoEngine




Geomatica OrthoEngine Module 4: Orthorectification


Lesson 4.1 - Generating the orthorectified images In this lesson you will:

Set up for orthorectification

Generate the four ortho photos

This lesson describes how to set up and perform orthorectification using aerial

photographs.

For this lesson you require:

The airphoto.prj file that you used in Module 2 to compute the math model. Alternatively, you may open the airphoto_model.prj file from Module 3, which

contains the four airphotos from the Richmond Hill dataset. The project

contains all the required GCPs and tie points and an up-to-date model

The ap_dem.pix DEM file.

Orthorectification is the process of using a rigorous math model and a digital

elevation model (DEM) to correct distortions in raw images as shown in the figure

below. The rigorous math models, such as the Aerial Photography or Satellite

Orbital math models, provide a method to calculate the position and orientation of

the sensor at the time when the image was taken. The DEM is a raster of terrain elevations.

The quality of the orthorectified image is directly related to the quality of the

rigorous math model and the DEM. A poorly computed math model, an inaccurate

DEM, or a DEM incorrectly georeferenced to the math model will cause errors in the

orthorectified images.

Figure 70. Using sensor geometry and a DEM to orthorectify imagery

Setting up for orthorectification

The Ortho Image Production window lets you set up and schedule the ortho

production. Several images can be selected and processed in one step.



Images to process

To set up the photos

1. Open the provided airphoto_ortho.prj file in OrthoEngine

2. On the OrthoEngine window in the Processing step list, select Ortho

Generation.

A new toolbar appears containing one icon to schedule the generation of the

ortho images.

Figure 71. Ortho Generation toolbar

3. Click Schedule ortho generation.

The Ortho Image Production window opens.

Figure 72. Ortho Image Production window

4. Under Available images, use the SHIFT key to select all four photos and

click the arrow button to move the images under Images to process.

The images are processed in the order that they are listed.

5. Under Images to process, select S129.

By default, the ortho image will be named oS129.pix. You could also enter a

different filename in the Ortho Image section.



Selecting the DEM

To select and load the DEM

1. In the Ortho Generation Options section, under Elevation Information,

select the DEM File radio button and click Browse.

2. From the AIRPHOTO folder, select the merged DEM file previously created and

click Open.

The Database Channels window opens.

NOTE: If you did not perform Module 3: DEM operations, then select

ap_dem.pix as your DEM file.

3. For the Background value, enter -32768.

This represents “No Data” pixels in the DEM. For DEMs generated by

OrthoEngine, the background elevation defaults to -150. Other DEMs have

different background elevation values that you must know before they can be

used. If you do not know the background value, click DEM Info in the DEM File

window. The window displays the three lowest and three heights values in the

DEM.

Note It is critical that the background elevation be set to the

correct value, if there are areas of no data in the DEM.

Elevation Scale

This is used to convert the pixel values in a digital elevation model (DEM) into

their correct elevation value. For example: since an 8-bit channel can only

contain integers between 0 and 255, you may have a DEM that was multiplied

by 10 to maintain the decimal precision of its elevation values. A DEM pixel

may have a value of 102, but the actual elevation that it represents is 10.2.

To convert the DEM pixel value from 102 to 10.2 you must multiply it by 0.1.

Therefore, you type 0.1 in the Elevation scale box to convert the DEM pixels

back to their true values.

Elevation Offset

This is used to add a value to the pixel values in a DEM to obtain their actual

elevation value. Perhaps the DEM pixel with a value of 102 actually represents

an elevation value of 1,102. To store the elevation values in an 8-bit channel,

1,000 was subtracted from all the pixel values when the DEM was created.

Therefore, you must type 1,000 in the Elevation offset box to restore the true values.

You can also use Elevation offset to adjust the elevation reference of a DEM.

The elevations in a DEM can be calculated above Mean Sea Level or an

ellipsoid. The elevation reference in the DEM must match the elevation

reference of the imagery that you want to orthorectify. To compensate for a

discrepancy, you can type the difference between the two elevation references in the Elevation offset box.

Note If you do not have a DEM, you can use the average

elevation of an area to orthorectify the image. However,

this will not produce results as accurate as using a DEM.

Type the average elevation in the Elevation offset box.

4. Click OK.



Sampling interval

The Sampling Interval controls how the computations are performed when an

image is orthorectified or geometrically corrected. When an image is

corrected, OrthoEngine selects a pixel from the output file, computes the

elevation from the DEM (if available), applies the math model to determine

which pixel location it corresponds to in the raw image, and then transfers the

data to the pixel in the output file. The Sampling Interval determines how

many output pixels are computed following this method. A Sampling Interval

of 1 means that the position of every output pixel is processed. To speed up

the process, you can increase the Sampling Interval. Note that this sampling interval interpolates the position of the pixels, not the intensity of the pixels.

To set up the sampling interval

5. Enter a value of 4

This means that the correction for every fourth pixel is calculated and the correction for the pixels in between are interpolated.

Selecting the resampling method

Resampling extracts and interpolates the gray levels from the original pixel

locations to the corrected locations.

Nearest Neighbor

Identifies the gray level of the pixel closest to the specified input coordinates

and assigns that value to the output coordinates. Although this method is

considered the most efficient in terms of computation time, it introduces small

errors in the output image. The output image may be offset spatially by up to half a pixel, which may cause the image to have a jagged appearance.

Bilinear Interpolation

Determines the gray level from the weighted average of the four closest

pixels to the specified input coordinates and assigns that value to the output

coordinates. This method generates an image with a smoother appearance

than Nearest Neighbor Interpolation, but the gray level values are altered in the process, which results in blurring or loss of image resolution.

Cubic Convolution

Determines the gray level from the weighted average of the 16 closest pixels

to the specified input coordinates and assigns that value to the output

coordinates. The resulting image is slightly sharper than one produced by

Bilinear Interpolation, and it does not have the disjointed appearance produced by Nearest Neighbor Interpolation.

To select the resampling method

6. In the Resampling list, select Cubic

Auto clip edge

This option removes a specified percentage of the image’s outside edge. You

can use this option to remove unwanted areas such as the data strip and

fiducial marks from aerial photographs or a dark perimeter or distortion along

the edge of the image.

Generating the orthos

You have two choices for the ortho generation:



You can generate your orthos now

You can generate your orthos at a later time

If you intend to automatically mosaic the processed images, you can click

Close instead of Generate Orthos. When you set up the Automatic Mosaicking

window, select Regenerate offline orthos, and OrthoEngine will process the

images and mosaic them in one step.

In this lesson, you will generate the orthos now.

To generate the ortho photos now

7. Click Generate Orthos

The Ortho Production Progress monitor opens and shows the status of the

orthorectification process for each photo. After all the orthos are generated, the following message appears: All Processing Completed.

8. Click Close The message Ortho done appears beside each photo in the Available images

section, indicating that the original photos are now orthorectified. The files

containing the corrected photos are named oS129.pix, oS130.pix, oS188.pix,

and oS189.pix.


Lesson summary

In this lesson you:

Set up for orthorectification

Generated the four ortho photos


Module 5: Mosaicking

About this module


Lesson 5.2 - Defining a mosaic Area

Lesson 5.2 - Manual mosaicking

Lesson 5.3 - Automatic mosaicking

Mosaicking is joining together several overlapping images to form a uniform image

as shown in the figure below. It is similar to creating a jigsaw puzzle with your images, and then making the seams disappear.

For the mosaic to look like one image instead of a collage of images, it is important

that the images fit well together. You will achieve better results if you orthorectify

your images. Using a rigorous math model ensures the best fit not only for the

individual images, but for all the images united as a whole.

To achieve that seamless look in the mosaic, place the seams, called cutlines,

where they will be the least noticeable and select images or portions of images that are not radically different in color.

Module 5: Mosaicking Geomatica OrthoEngine


Figure 73. Mosaicking

Manual mosaicking

You can use Manual Mosaicking to create your mosaic one image at a time, to edit

the cutlines in an automatically mosaicked project or to replace unsatisfactory

areas in the mosaic. For each image that you want to include in the mosaic file, you

must complete four steps in sequence; select an image to add, collect the cutline, adjust the color balance and add the image to the mosaic area.

Automatic mosaicking

Although you can create your mosaic one image at a time by using Manual

Mosaicking, most of the time you will use Automatic Mosaicking to do the bulk of

the work, and you will use Manual Mosaicking to edit portions of the mosaic file.

Some projects may require more editing than others such as those containing large

bodies of water or urban areas with buildings leaning in different directions. In

addition to reducing your work load, Automatic Mosaicking will often produce a more seamless look than if you had attempted to create the mosaic by hand.

Both manual and automatic mosaicking will be described in detail in this module

although as stated above, automatic mosaicking would generally be used to do the bulk of the work. Manual mosaicking could then be used to edit the cutlines.

Geomatica OrthoEngine Module 5: Mosaicking






Lesson 5.1 - Defining a mosaic Area In this lesson you will:

Define an area for mosaicking

To define an area for mosaicking, you require:

The file airphoto.prj that you used in Module 4 to generate the ortho images.

Alternatively, you can open airphoto_ortho.prj from the AIRPHOTO folder.

This project contains all the required ground control points and tie points, and the photos are already orthorectified.

The objective of this lesson is to set up an area that includes a part of each of the

four orthophotos. The defined area is used in the mosaicking process.

Defining a mosaic area

The Mosaic Area determines the extents of the mosaic file. The images are added to

the Mosaic Area like pieces of a puzzle. On the Define Mosaic Area window, the

footprints of the images in your project are displayed as they overlap. The crosshair

represent the principal point of each image. Click one of the crosshair to reveal the

footprint of an individual image. The background value of the Mosaic Area is zero by default.

Before you create a mosaic, you need to define an empty mosaic file.

To define the mosaic area

1. On the OrthoEngine window in the Processing step list, select Mosaic.

A new toolbar with one icon appears. The toolbar contains functions for

mosaicking.

Figure 75. Mosaic toolbar

2. On the Mosaic toolbar, click Mosaicking.

The Mosaic Tool and New Project Wizard window opens. You will notice the

previously orthorectified images in your project are automatically added to

the Source file list.

3. From the Source file list, shift and select all of the images so that the

footprints appear highlighted cyan.



Figure 76. Define Mosaic Area window

By default the bounds of the Mosaic Area are the maximum extents of the images

in the project. The size of the mosaic area can be changed manually by clicking the Define mosaic extents icon.

4. Enter the define mosaic mode by clicking the Define mosaic extents icon

5. Place the cursor over the side or corner of the frame inside the Mosaic Area

and move it to change its size and shape.

6. Use the mouse cursor to change the size, shape, and position of the mosaic

area. Define a broad area that includes part of each photo in the project.



Figure 77. Define Mosaic Extents, resizing the mosaic extents

7. Click Next

8. In the Output file section, click Browse.

The file selector window opens.

9. For the name of the file, enter manual_mosaic.pix and click Save.

Alternatively, you may select an existing mosaic file.

To select an existing mosaic file

1. Next to Mosaic extents, select the Define From File… button.

The File Selector panel opens.



Figure 78. File Selector window

2. Select the mosaic file air_mos.pix and click Open.

Selecting images for mosaicking

By default, all images in the project are selected; these appear as green frames in

the Mosaic Area window. You may choose to select fewer images for the mosaic.



Figure 79. Selecting images to include in mosaic

The following image shows an incorrect mosaic definition because the mosaic area

overlaps only sections of unselected images; this mosaic area will display an error message.

Figure 80. Incorrect mosaic area definition — no overlap with selected images

1. Click Finish.

2. The images are automatically added to the Mosaic Tool window.

OrthoEngine creates or saves your mosaic file and stores it in the folder where

your project is saved.

You are now ready to mosaic your photos.

With OrthoEngine there are two methods for generating a mosaic:

Manual mosaicking


Manual mosaicking

First, use the manual process in Lesson 5.2: Manual mosaicking to create a

seamless final product from the four orthophotos. Here, you use a set of manual tools, which affords more precise control.


After this process is complete, proceed to Lesson 5.3: Automatic mosaicking and

mosaic the same set of orthophotos using the automatic tools.

Lesson summary

In this lesson you:

Defined an area for mosaicking

Selected images for mosaicking





Lesson 5.2 - Manual mosaicking In this lesson you will:

Select images to add to the mosaic

Collect and edit cutlines

Perform color balancing

Generate the mosaic

About this lesson

To create the mosaic from the orthophotos, you require:

The project file from Lesson 5.1

Mosaicking the first image

After defining an area, begin manual mosaicking by selecting the first photo to add

to the mosaic file.

Figure 81. Manual Mosaicking window

Project image files

Images within the current project are listed in the Tree List. Images are identified

by a crosshair within the viewer. Your mosaic file is empty; therefore, you do not

need to collect cut lines for the first image. The first image, oS129.pix, will be added to the mosaic file in its entirety.



Collecting and editing cutlines

When creating a mosaic, you want to crop the images so the best portions of the

images are seamlessly joined together. A cutline is a polygon that outlines the portion of an image that will be used in the mosaic.

As the cropped images are added to the Mosaic Area, the data in overlapping areas

is covered by the most recent addition. Areas where several images overlap provide

you with more opportunities to find the best location for the cutlines. When you save the project, the cutlines are saved with their corresponding images.

To make the seams between images less visible, select features that are consistent

in tone and texture, low to the ground, uniform in appearance, and conspicuous,

such as roadways and river edges. Features that display clear boundaries provide a natural camouflage for the seam.

Avoid:

buildings or man-made features, since they may lean in different directions in

the imagery

large bodies of water, because waves may look different in different images,

and water tends to have different color in different images

areas that are significantly different in color and texture, such as forests and

cultivated land, since they may look different from image to image

Collect cutlines

To collect cutlines

1. From the tree list, select the oS130.pix image, and turn off the images

oS188.pix and oS189.pix.

2. Right-click oS130.pix to display the context menu, and select New Cutline.

Alternatively, click the New Cutline icon on the toolbar.

Figure 82. New Cutline context menu item



A crosshair with polygon appears when the mouse is placed over the corresponding image.

3. Using the zoom tools, zoom to a road that runs through the area of overlap.

4. Using your mouse, create a cutline for the area of the oS130.pix image that

you wish to include in the mosaic.

Clicking with the mouse will add a vertex to the polygon. Double-click to

complete the cutline.

Figure 83. New cutline created

The collected cutlines appear as vector layers under the corresponding image label in the tree list.

The changes resulting from cutline collection are shown in the Mosaic Tool view

area as the changes take place.

Edit cutlines

To edit a cutline

1. From the toolbar, click the Vector Editing icon.

The Vector Editing Tools panel opens in the toolbar.

Figure 84. Vector Editing Tools panel(outlined in red)



2. Use the Vector Editing tools to move, delete, add new vertices, and reshape

a cutline. You first need to select a cutline by clicking on it with the Find tool

selected.

3. Click Show Vertices to make all vertices on the selected cutline visible

4. Click on the vertex that you wish to edit and move it using the mouse

Figure 85. Editing a cutline

5. Move your mouse cursor to the road with the cutline, then click Zoom to 1:1

Image Resolution. As you can see, that part of the cutline is off the road,

and the line does not have enough vertices to correct it.

Figure 86. Correcting a cutline

6. Click the Add Vertices icon on the Vector Editing Tools panel and move the

cursor to the cutline. Each mouse click adds a new vertex to the cutline.



Figure 87. Adding new vertices to the cutline

7. Add 5 or 6 vertices to your cutline and move the new vertices to position the

cutline on the road. The cutline is now correct, but it also contains a few

unnecessary vertices.

8. Click on the vertices that you don’t need, and press the Delete key on your

keyboard to remove them.

An alternate method for fixing this cutline would be to use the Reshape tool.

This tool allows you to conveniently insert and move vertices along a cutline.

9. Click the Reshape tool on the Vector Editing Tools panel.

10. Using your mouse, click on the image near a cutline where you want the new

reshaped cutline to be placed.

11. Continue to click along the feature in the image where the cutline should be

located.

A thin black line will appear; this represents the reshaped cutline. A circle will

also be located along the existing cutline, to indicate which cutline

vector/vertices will be reshaped.

Remember that it does not matter on which side of the existing (incorrect) cutline

you click. Because the goal in this case is to correct an existing cutline, click on the feature where the cutline should go.



Figure 88. Reshaping a cutline

In Figure 88, notice that the user digitized in the center of the road and crossed the

cutline numerous times.

Figure 89. Reshaped cutline

The end result is a reshaped cutline that correctly follows the feature, as modified by the user.



12. To add, move, or select vertices, you may also use the Vertices window.

Select vertices on your cutline, click the Show Vertices icon on the

toolbar, then click the Vertices… icon .

The Vertices window opens.

Figure 90. Vertices window

13. Select a vertex from the table; this vertex will also appear selected in the

Mosaic Area display. Selecting a vertex on the cutline also selects it in the

Vertices table.

14. From the Vertices window, click the + button. A new vertex is added to the

cutline. Add a few more vertices to correct your cutline.

15. Select a vertex and click the - button to delete it.

16. Select and expand a layer in the Layer List of the Mosaic Tool window by

clicking the expand button to the left of the layer.



Figure 91. Cutline context menu

17. Right-click the cutline layer. The following editing tool are available:

Import Cutline Shapes... to load an existing vector cutline layer

Delete Cutlines to remove the cutlines created for that particular

layer

Line – Solid #1 Color… to change the vector entry color

Select Style… to change the vector entry style

Set Blend Width to specify the blend width for the image and the

neighboring images

Load cutline

You can load a previously exported cutline, or any polygon vector.

To load a cutline

1. From the context menu described above, click Import Cutline Shapes...

The Import Cutline window opens.



Figure 92. Import Cutline window

2. Click Browse to select the file that contains the cutline.

3. In the Shapes section, select the desired layer and the polygon shape within

that layer, then click OK.

The cutline appears in the Mosaic Area display.

Blend seams

Blending reduces the appearance of seams by mixing the pixels values on either side of the cutline to achieve a gradual transition between the images.

In Automatic Mosaicking, OrthoEngine blends the seams automatically. In Manual

Mosaicking, the Blend Width option determines the number of pixels on either

side of the cutline that are used to blend the seam. In areas containing bright or

significantly different features, however, setting the Blend Width too high may cause "ghosting" or doubling of the features.

To set the Blend Width

1. Expand the oS129.pix image in the Tree Layer.

2. Right-click the Cutline vector layer to display the context menu, and select

Use blend width. Set the Blend Width to 3 pixels.

3. Click OK.

A blend width of three to five pixels is recommended for most mosaicking

projects.

Adjusting the color balance

Radiometric differences between images can cause a patchwork effect in a mosaic.

Color balancing evens out the color contrasts from one image to another to reduce

the visibility of the seams and produce a visually appealing mosaic.



You can adjust the color balance in Manual Mosaicking by collecting samples in the

overlap between the images already mosaicked and the image that you are adding

to the mosaic. OrthoEngine uses these samples, which are referred to as Match

Areas, to compute a look-up table (LUT) that will adjust the color in the image that

you are adding to match the images already mosaicked.

Collect small match areas representing the different areas so the look-up table can

be used to accurately correct radiometric mismatches. For example, collect a match

area in green areas to balance greens, a match area in dark areas to match dark

values, a match area in urban areas to match urban areas, and so on. Using a

single large match area covering a large part of the image is effective only if you

have an overall bright or dark difference between the images.

Create match areas

To create match areas

1. Select the oS130.pix image in the Layer List and click the New Match Area

button to begin the color balancing process.

Alternatively, right-click the oS130.pix image to display the context menu,

and select New Match Area.

The crosshair and polygon icon appears.

2. Expand the oS130.pix group and ensure that the Match area layer is visible

by checking it.

3. Draw a polygon to define the match area on the selected image in the Mosaic

Tool window.

The polygons over the match areas will be filled with the color corresponding

to the cutline for the image chosen.

Note You can edit match areas using the same procedure as described for editing cutlines. See Edit cutlines on

page 126.

4. To apply the color balancing to the image, click the dropdown on the color

balancing button and select Manual Area.



Figure 93. Color Balancing, Manual Area

5. Define several match areas using the manner described above.

Any number of match areas can be defined, and are displayed in the viewer.

As you collect Match Areas, the mosaic window displays the changes in the

color balancing.

After the color balancing step is completed and you are satisfied with the results

displayed in the mosaic window, proceed to generating the mosaic.

Generating the mosaic

When you have collected cutlines and performed color balancing for the images to

be mosaicked, you can add the image(s) to the output mosaic file.

To generate the mosaic

1. From the Layers List, select images by holding down the Ctrl key and

selecting each file. Select oS129.pix and oS130.pix.

2. On the toolbar, click the Add to Mosaic icon.

A progress monitor opens, indicating the image is being mosaicked into the

mosaic area file.

When the process is complete, the mosaicked images are removed from the

Tree List; the image layers are listed under the Output mosaic file. These

images can be reprocessed.

You have now created a mosaic of two photos using manually collected cutlines. Continue to add the remaining photos.

Adding and removing images

Add an image

You can add images that you did not select when you defined the Mosaic Area.



To add an image to the mosaic

1. From the Layers List, select images by holding down the Ctrl key and

selecting each file. Select oS188.pix and oS189.pix.

2. Click Add, click the Add to Mosaic icon.

Both images, along with their cutlines, open. You can process these images as

previously described, or remove them from the Tree List.

Remove an image

You can remove images that you no longer want in your final mosaic.

To remove an image from the mosaic

1. From the Tree List, select the image that you want to remove.

2. Right-click to display the context menu, and select Remove Image.

The image is removed from the current session.

Note Although you can select multiple images to add to the

session, you may only remove a single image at a time.

Reprocessing images

After the mosaic is complete, you may discover some areas that you want to

change. You can use the Restore to source… option accessed by right clicking the

Output mosaic heading on the Tree List to edit the cutlines or adjust the color balancing for the images as required.

You can use the Restore to source… option to:

1. Reproduce missing mosaic files.

2. Regenerate the mosaic at different resolutions.

3. Create a subset of the mosaic by changing the size of the Mosaic Area and

then regenerating the mosaic.

To regenerate the mosaic

1. In order to reapply mosaicking, OrthoEngine must have access to a blank

mosaic file to which the results will be written. To create this blank mosaic

file, select the Define Output Mosaic…option accessed by right clicking

Output mosaic from the Tree List.

2. Select Define Output Mosaic… and in the output file field, enter a new file

name for the blank mosaic file.

3. Click OK.

4. On the Tree List hold the shift button on your keyboard and select the images

you want in your mosaic. Click the Add to Mosaic button on the toolbar.

5. A progress bar will appear stating that the mosaic is being regenerated.

Lesson summary

In this lesson you:

Selected images to add to the mosaic

Collected cutlines

Performed color balancing

Added images to the mosaic



Generated the mosaic



Lesson 5.3 - Automatic mosaicking In this lesson you will:

Define a new mosaic area

Set up for automatic mosaicking

Generate the mosaic

View the mosaic

About this lesson

Mosaicking is the second step in the Data Correction stage. This lesson describes

how to set up the orthophotos, generate the mosaic automatically, and view the

result. In order to use the automatic mosaicking functions you must have an Ortho

Production Toolkit license.

To create the mosaic from the orthophotos, you require:

The project file from Lesson 5.1

An new area defined for mosaicking

Defining a new mosaic area

Before creating your automatic mosaic, you must define a new file for the mosaic.

Otherwise, the manual mosaic will be overwritten.

To define a new mosaic area

1. From the OrthoEngine toolbar select Mosaic from the Processing step

dropdown menu. Select the Mosaicking icon. The New Project Wizard opens.

2. Shift and select all of the images in the Souce file list. Click Next >>

3. Select an Output file location by clicking Browse…

4. Type auto_mosaic.pix as the file name.

5. Click the Define mosaic extents icon and place the cursor over the side or

corner of the frame to change its size and shape.

Alternatively, you can enter corner coordinates for the mosaic area.

Define a broad area that includes part of each photo in the project.

6. Click Next>>

Mosaicking images automatically

Although you can create your mosaic one image at a time by using Manual

Mosaicking, most of the time you will use Automatic Mosaicking to do the bulk of

the work, and you will use Mosaic Tool to edit portions of the mosaic file. Some

projects may require more editing than others such as those containing large

bodies of water or urban areas with buildings leaning in different directions. In

addition to reducing your work load, Automatic Mosaicking will often produce a more seamless look than if you had attempted to create the mosaic by hand.



The mosaicking process starts with the image selected in the Starting image list

and then adds contiguous images moving outward from the Starting image. Once

the Starting image is selected, the list is sorted according to the order that they will

be added to the mosaic file. The Starting image also determines the order for color

balancing and cutline generation.

Now that you have defined a new mosaic area, you will create the mosaic using the

automatic mosaicking tools. Afterwards, you will compare the manually generated mosaic with the automatically generated mosaic.

To select a cutline method

1. From the Compute Cutlines Method list, select Minimum squared difference.

2. You can select a global or local mask if you have one for your data.

3. Type 5 Pixels as the Blend width

This is the number of pixels on each side of the cutline to delimit the area for

smoothing the radiometric differences between the images. A Blend width of

5 will create a total blending width of 10 pixels - 5 pixels on either side of the

cutline.

Cutlines

Cutlines are drawn in areas where the seams are the least visible based on the radiometric values of the overlapping images. There are seven methods:

Minimum squared difference

This method is suitable for most mosaicking projects, and in most cases produces

the cleanest cutlines. The algorithm determines a cutline in each overlapped area

between two adjacent images, with minimum-squared differences of gray values at the same locations of the region in all image channels.

Minimum difference

To place the cutline in areas where there is the least amount of difference in gray values between the images.

Minimum relative difference

To place the cutline in areas where there is the least amount of difference in gradient values between the images.

Edge

To use a combination of Min difference and Min relative difference to determine the optimum location for the cutline.

Maximum data

Places the cutlines on the boundary of the real-image pixels, meaning that No Data

pixels are ignored when determining the image boundary.

Import

Use polygons in a vector file you specify.

The cutlines are computed exactly as per the vector file.

File extents

Computes the cutlines as the extents of the input imagery.

NoData pixels are included in computing the cutlines.



To apply color balancing during mosaicking

1. Select Apply color balancing

2. Select Bundle as the color balancing Method from the drop down list.

Color balance

Automatic color balancing applies tonal and contrast adjustments over the mosaic.

There are five methods:

None

No color balancing is applied.

Bundle

The Bundle color-balancing method is recommended for most image mosaics. It

uses two main steps to balance the overall mosaic.

The first step computes the statistics for all overlapping image areas after

automatically removing anomalies in the data, such as clouds, snow, and so forth.

A bundle color adjustment is then applied globally to minimize the overall

differences between all overlapping areas. This adjusts both the mean and sigma

(brightness and contrast).

In the second step, the remaining differences are modeled with dodging. Dodging adjusts pixel values to try to minimize differences between images.

Histogram

The histogram of each entire image is used to compute the color balancing

histogram. This method is recommended for images with low overlap or for images

with systematic effects such as when images are bright at the top and dark at the bottom.

Overlap area

This method computes the color balancing histogram using only the pixels in the

overlapping area of the images being added to the mosaic file. This method is

recommended for most images.

Reference image

This method matches the color balancing for the mosaic to the image identified in

the Mosaic reference image box.

Look up Table

This method is relevant only to images in PCIDSK format and that have pre-existing

lookup tables (LUT); it is used rarely in an automated workflow. The LUT color-

balancing method takes each image channel and its corresponding manually created LUT and applies it to the mosaic scene by scene.

Neighborhood

This method is best suited to large-area mosaics, in which features tend to change

from one end of the mosaic to the other. On average, the processing time of this

method is about twice as long as other color-balancing methods.



Starting image

The Starting image list lets you select the corrected image to be the basis for the mosaic, the color balancing, and the cutline selection.

To select the Starting image

1. Select Sort images.

2. Select Nearest to center as the Method.

3. In the Starting image list, select (Auto).

To apply normalization during mosaicking

1. Under Normalize select None as the Method.

Normalization

Normalization is used to even out the brightness in the images to achieve a

more pleasing mosaic.

None

To leave the images as is.

Hot Spot

To remove hot spots from the image. A hot spot is a common distortion that

results from solar reflections. Hot Spot normalizes the brightness over the image, but it does not remove spot reflections from lakes, cars, and buildings.

Adaptive filter

To remove high frequency noise while preserving high frequency features such

as edges.

Generating the mosaic

To generate a mosaic preview

1. Click the Generate Preview button.

This creates a low resolution version of the mosaic. You can use the preview of

the mosaic to verify the color balance and cutline generation before continuing with the full resolution version.

A process window will appear. Once the process is complete the mosaic preview

will appear in the New Project Wizard window.



Figure 94. Automatic mosaic preview

To generate the mosaic

1. Click the Generate Mosaic button.

This creates the full resolution version of the mosaic. It is saved in the auto_mosaic.pix file you created.

Viewing the mosaic

When you complete the automatic mosaicking, you can easily inspect the results in

the image viewer, the Mosaic Tool or in Geomatica Focus.

1. Click Finish.

The mosaic preview or the generated mosaic is now loaded into the Mosaic

Tool for further review and editing.



Figure 95. Mosaic Tool, Mosaic preview. Images loaded to Source images in the

Tree list for further editing

Figure 96. Mosaic Tool, Mosaic generation. Mosaic loaded to Output mosaic in the

Tree list

2. Alternatively, you can open the mosaic in a Focus window.



From the AIRPHOTO folder, double click auto_mosaic.pix. The Focus

window opens and displays the mosaic file you created.


Lesson summary

In this lesson you:

Defined a new mosaic area

Set up for automatic mosaicking

Generates the mosaic

Viewed the mosaic

Module 6: OrthoEngine componentization Geomatica OrthoEngine


Module 6: OrthoEngine componentization

About this module


Lesson 6.1 - Data input and GCP collection

Lesson 6.2 - Project creation and tie point collection

Lesson 6.3 - Automatic GCP collection and mosaicking

OrthoEngine components

Many functions which were previously available only in the Focus and OrthoEngine

GUI environments have been componentized. Componentization means that these

functions can now be linked together into automated workflows and run in batch

processes. Automation and batch processing of workflows is available through both visual modeling and command-line scripting.

Component tasks include:

Project creation

Data import

Ground control and tie point collection

Radiometric adjustments

Orthorectification

Modeler

Geomatica Modeler provides an interactive methodology for the development of

both simple and complex data processing flows. Modeler provides access to a

number of standard operations such as data import and export, as well as most

EASI/PACE processing algorithms.

You build processing models by placing modules on the Modeler canvas and then

connecting the modules with pipes to create a process flow. You first configure the

modules and then execute the model in either single execution mode or batch

mode. During the execution of the model, graphical cues indicate the data flow through the process. The Module Librarian enables quick access to all modules.

Geomatica OrthoEngine Module 6: OrthoEngine componentization


Lesson 6.1 - Data input and GCP collection In this lesson you will:

Launch Modeler and add modules to the canvas

Connect the modules in the model

Fill in the parameters for the Modeler modules

Run the model in single execution mode

About this lesson

A common operation in desktop photogrammetry is the registration of new images

to existing geocoded images. Often this entails registration from a previous year’s

data, or the updating of a database with new overlapping imagery. Traditionally,

this registration was done through the manual or semi-automated collection of

ground control points. Geomatica 2015 includes automated ground control

collection through automatic image-to-image registration. Combined with

OrthoEngine’s accurate satellite and airphoto models this technology enables fast,

automated, rigorous orthorectification.

The model for this lesson uses a total of seven modules from the Module Librarian.

First, you will add a CDLAND7 module to the canvas to import a Landsat-7 scene in

HDF to pix format. The IMPORT modules will import a geocoded image and a digital elevation module.

AUTOGCP will be used to collect GCPs from the geocoded image. These GCPs will be

refined with GCPREFN. SATMODEL applies the satellite model after which the image can be orthorectified and exported with ORTHO and EXPORT, respectively.

To start Modeler

1. On the Geomatica toolbar, click the Modeler icon.

Figure 97. Geomatica toolbar Modeler command

The Module Librarian and the Modeler workspace open on your desktop.



Figure 98. Modeler workspace

Figure 99. Module Librarian

The Module Librarian provides access to the modules you can use to process

your data. Modules are the basic building blocks for your model. You access

modules from the Algorithm Library tree view in the Module Librarian.

Modules are sorted into categories and subcategories according to their functionality and can also be listed alphabetically.



Note Modules for which you are not licensed are identified with

a lock icon.

To place a module on the canvas

1. In the Module Librarian, expand the All Algorithms folder.

2. Select the CDLAND7 module.

The CDLAND7 graphical element displays in the Selected Algorithm area.

3. Click the CDLAND7 graphical element in the Selected Module window.

4. Click anywhere on the canvas.

The CDLAND7 graphical element displays on the canvas.

Note You can also click Add to Canvas to place the selected

module in the canvas.

5. Add the following modules to the Modeler canvas:

2 IMPORT modules

1 SPLIT module

1 AUTOGCP module

1 GCPREFN module

2 SATMODEL module

1 ORTHO module

1 EXPORT module

Your canvas should appear similar to the one below.

Figure 100. Modules arranged on canvas

Now that all the modules have been added to the model, you will configure the

IMPORT modules.

To configure the first IMPORT module

1. Double-click the IMPORT module located directly below the CDLAND7 module

The IMPORT Module Control Panel opens

2. On the Input Params 1 tab, click Browse and locate the LANDSAT folder



3. Select geo_landsat.pix and click Open

The Available Layers from this file are listed

4. From the Available Layers list, select 3 [8U] red band

Note If using multispectral data for automatic image to image

registration, you may choose to avoid using the blue band as it

tends to be noisier than the other bands.

Figure 101. IMPORT Module Control Panel

5. Click Accept

The output raster port glyph displays on the IMPORT module and the status

indicator bar turns green

To configure the second IMPORT module

1. Double-click the second IMPORT module

The IMPORT Module Control Panel opens

2. On the Input Params 1 tab, click Browse and locate the LANDSAT folder

3. Select dem.pix and click Open

The Available Layers from this file are listed

4. From the Available Layers list, select the DEM from raster files layer

5. Click Accept

To configure the CDLAND7 module:



1. Double-click the CDLAND7 module

The CDLAND7 Module Control Panel opens

2. On the Input Params 1 tab, click Browse

The File Selector window open

3. From the LANDSAT\raw folder, select L71048026_02620000923_HDF.L1G

file and click Open

4. In the CD Input Layer(s) List box, type 3, 2, 1, 4, 5

Since you will be using the Red TM band from the geo_landsat.pix file to

collect GCPs on the raw Landsat image, you will want to order the LANDSAT 7

Bands such that the Red band is first in the list. This makes it easier for

setting up the GCP collection step later in the model.

5. In the File Description box, type Southern Vancouver Island

Figure 102. CDLAND7 Module Control Panel

6. Click Accept.

The status indicator bar turns green.

You will now connect the modules with pipes.

Pipes

Pipes are graphical elements that represent data transmission paths between

modules. A pipe can be “thin” or “fat”. A thin pipe contains only one layer of

information. A fat pipe, which is wider than a thin pipe, contains multiple layers of information.



Modules are connected by clicking the output port of the module to connect from

and then clicking the input port of the module to connect to. You can also connect

between a pipe and a module by first clicking the pipe, and then clicking the input port on the connecting module.

A pipe is default color-coded according to the type of data that it transmits. Some

examples are listed below.

Pipe Colors and Data Types

Color Type

Green Rasters

White Vectors

Red Bitmaps

Blue Pseudocolor tables

Yellow Look-up tables

Cyan Binary

Black Dead pipe

Note The most frequent causes for dead pipes are moving, renaming, and

deleting an input file or layer.

Always connect modules before you configure them, because for some

modules the default settings of an input layer can override a module’s

configuration. An exception is the IMPORT module, which you must

configure for it to display a port.

To connect CDLAND7 and SATMODEL (left module)

1. Click the raster output port from CDLAND7 and then click the input port on

the SATMODEL module

A green pipe connects these two modules

2. Click the Satellite Orbital Ephemeris Layer port from CDLAND7 and then click

on the Orbit layers Input Port on the first SATMODEL module.

A blue pipe connects these two modules

To connect CDLAND7 and SATMODEL to AUTOGCP

3. Click the raster output port from CDLAND7 and then click the input port on

the SPLIT module

A green pipe connects these two modules

4. Click the first output port from the SPLIT module and connect to the Input

Image Layer port on AUTOGCP

5. Click the Output Math Model Layers port from the first SATMODEL module

and connect this to the Math Model Layer on the AUTOGCP module.

A cyan pipe connects these two modules

Note A SPLIT module will split a 'Fat' pipe into a 'Thin' pipe. Since a 'fat'

pipe contains many layers, while a 'thin' pipe contains only 1 layer,

this module will be transferring 1 layer from a 'fat' pipe and placing

it into its own 'thin' pipe. This is useful if only a particular layer

needs to pass on to another module.



To connect the IMPORT modules to AUTOGCP

1. Click the raster port from the first IMPORT module, containing the

geo_landsat.pix and connect this to the Reference Image Layer raster port on

the AUTOGCP module

2. Click the raster port from the second IMPORT module, containing the

dem.pix and connect this to the Elevation Layer port on AUTOGCP

Figure 103. CDLAND7 and IMPORT pipe connections to AUTOGCP

To connect AUTOGCP and GCPREFN

1. Connect the GCP Layer port from AUTOGCP to the GCP Layer to be Refined

port on GCPREFN

A cyan pipe connects these modules.

2. Connect the output math model port from the first SATMODEL to the input

math model port on GCPREFN

To set up the connections for SATMODEL

1. Connect the output GCP port from GCPREFN to the GCP port on the second

SATMODEL

2. Connect the output raster port from SPLIT to the raster input port on the

second SATMODEL

3. Connect the output orbit layer port from CDLAND7 to the orbit layer port on

the second SATMODEL.



Figure 104. Pipe connections to SATMODEL module

To set up the connections for SATMODEL and ORTHO:

1. Connect the output raster port from CDLAND7 to the Image Layers to be

Processed raster port on ORTHO

2. Connect the output raster port from the second IMPORT to the Elevation

Layer raster port on ORTHO

3. Connect the Output Math Model Layer port from the second SATMODEL to

the Math Model Layer port on ORTHO

4. Connect the raster port from ORTHO to the input (or ‘any’) port on EXPORT

Figure 105. Model to orthorectify a Landsat-7 image



To configure the AUTOGCP module

1. Double-click the AUTOGCP module

2. For the Number of GCPs per image, enter a value of 128

This is the number of candidates that will be used for the matching process.

The default values for the remaining parameters will be used

3. Click Accept

To configure the GCPREFN

1. Double-click the GCPREFN module

2. For the Rejection Method, select Absolute Distance (4) from the dropdown

list

3. Set the Minimum X & Y Absolute Residual to 1

4. Click Accept

For this model, there are no parameters to configure for either of the SATMODEL modules. The Output Projection for the ortho images will be set in the ORTHO MCP.

To configure the ORTHO module

1. Double-click the ORTHO module.

2. On the Input Parms 1 tab, enter UTM 10 U D000 for the Output Projection.

3. For the Output Pixel Ground Size: X, Y, enter 30, 30.

4. Set the Resample Mode to Cubic.

5. Click Accept.

To configure the EXPORT module

1. Double-click the EXPORT module.

2. On the Input Params 1 tab, click Browse and locate the LANDSAT folder.

3. For the File name, enter LandsatOrtho.pix and click Save.

4. Select the Overwrite Existing File option.

If the model is run more than once, LandsatOrtho.pix will be overwritten.

5. Click Accept.

Note The COMMENT module allows you to enter comments

relevant to the particular model.

To add comments

1. Add a COMMENT module to the canvas and place it above the CDLAND7

module.

2. Double-click the COMMENT module.

3. On the Input Params 1 tab, enter Read Landsat Imagery from HDF file.

4. Click Accept.

5. Resize the comment box so all the text is visible.



Figure 106. Model with comment

6. Add any other comments you would like to the model.

You are now ready to execute your model.

To execute a model

1. From the Execute menu, select Run or click the Run button on the toolbar.

The status indicator bars on the modules show the progress of each operation

as it is executed. Another progress indicator in the display area of the Modeler

window monitors the progress of the entire model. It will take a few minutes for the model to run to completion.

When the execution of the model has completed, double-click the AUTOGCP and

GCPREFN modules and examine the reports in the LOG tab.

To see how well the ortho image is registered to geo_landsat.pix, open the files in

Focus.

Saving a Model

Now that you have created your first model with Modeler, you will save the model

as a MOD file.

To save your model

1. From the File menu, select Save Model.


2. Navigate to the LANDSAT folder.

3. In the File Name box, type ortho_landsat.mod.

4. Click Save.

The File Selector window closes and your model is saved as a MOD file.

5. From the File menu, click Close Model.



Note When you save a model after it has executed, the

intermediate and output files are not saved with the

model.

Lesson summary

In this lesson you:

Launched Modeler and added modules to the canvas

Connected the modules in the model

Configured the modules in the model

Ran the model in single execution model



Lesson 6.2 - Project creation and tie point collection In this lesson you will:

Add modules to the canvas

Configure the modules for batch execution


Run the model in batch execution model

OrthoEngine projects can now be created and saved in Modeler. This allows you to

set up a work flow in Modeler to perform some of the image processing of your data. You can then continue working in OrthoEngine with the exported project file.

The model for this lesson uses a total of seven modules from the Module Librarian.

First, you will batch import two ASTER images. An OrthoEngine project file will be

created for the data and tie points will be automatically collected and refined. The

project file will be saved once the tie points have been collected and could later be

used in OrthoEngine for further data extraction. CPMMSEG will apply the sensor model and the images will be orthorectified with ORTHO.

To start setting up the model


2 IMPORT modules

1 ACCUMULATE module

1 CRPROJ module

1 AUTOTIE module

1 TPREFN module

1 CPMMSEG

2 RELEASE modules

1 ORTHO module

2 EXPORT modules


Figure 107. Modules on canvas with no pipe connections



To configure the first IMPORT module

1. Double-click the first IMPORT module.

2. The IMPORT Module Control Panel opens.

3. Click Browse and navigate to the ASTER_MODELER folder.

4. Select 3b.pix and click Open.

5. From the Available Layers list, select the raster layer.

6. Click Batch.

7. The Module Control Panel expands.

8. Right-click on the file column heading and select Add Files.... Select the

3n.pix file. You should have two entries, one with 3b.pix and one with 3n.pix.

Figure 108. IMPORT Module Control Panel with Batch parameter sets

9. Click Accept.


1. Double-click the IMPORT module above the ORTHO module.

2. Click Browse and navigate to the ASTER_MODELER folder.



3. Select aster_dem.pix and click Open.

4. From the Available Layers list, select the 32R SRTM DEM layer.

Figure 109. IMPORT Module Control Panel

5. Click Accept.

Note The pseudo GCPs delivered with ASTER data cannot be

imported into the model because the 3N and 3B images

have different GCPs with the same GCP ID.

The modules will now be connected with pipes.

To connect the first IMPORT and CRPROJ modules

1. Click the raster port from the first (leftmost) IMPORT module and connect

with the input port of the upper ACCUMULATE module.

2. Connect the output port from the ACCUMULATE module to the raster port on

the CRPROJ module.

Note The ACCUMULATE module accumulates all incoming layers

during batch execution. These layers are released when

all batch runs have been exhausted and all modules

executed.



Figure 110. IMPORT module connected to CRPROJ

To connect CRPROJ to CPMMSEG and EXPORT

1. Connect the Output OrthoEngine Project port from CRPROJ to the Input

OrthoEngine Project port on AUTOTIE.

2. Connect the Output OrthoEngine Project port from AUTOTIE to the Input

OrthoEngine Project port on TPREFN.

3. Connect the Output OrthoEngine Project port from TPREFN to the Input

OrthoEngine Project port on the first EXPORT module.

4. Connect the Output OrthoEngine Project port from TPREFN to the Input

OrthoEngine Project port on CPMMSEG.

5. Connect the Output raster port from the ACCUMULATE module to the Input

raster port on the first RELEASE module.

6. Connect the Output raster port from the first RELEASE module to the Input

raster port on the CPMMSEG module.

Note The RELEASE module releases all incoming layers in

sequence by layer or by group during batch execution.

Release takes place during each model execution until all

groups or layers have been released.



Figure 111. Connections from CRPROJ

To connect ORTHO and EXPORT

1. Click the output raster port from the ACCUMULATE module and click the

input port on the second RELEASE module.

2. On the second RELEASE module click the output port and connect it to the

input Image Layers to be Processed raster port on the ORTHO module.

3. Connect the output Math Model Layer port from CPMMSEG to the input Math

Model port on the ORTHO module.

4. Connect the output Raster port of the second IMPORT module to the input

Elevation Layer port of the ORTHO module.

5. Connect the output Raster port from ORTHO to the input port on the

EXPORT module.

Figure 112. Model showing all pipe connections



Now that all of the modules are connected, you will now configure them.

To configure the CRPROJ module

1. Double-click the CRPROJ module.

This module creates an OrthoEngine Project file

2. For the Output Projection, enter UTM 34 T D000

3. From the Model Type list, select SAT (Toutin’s Model)

4. Click Accept

To configure the AUTOTIE module

1. Double-click the AUTOTIE module

This module performs automatic tie point collection from a pair of overlapping

images

2. On the Input Params 1 tab, set Number of Points per Area to 128

3. For Distribution, select Overlap

4. For the Search Radius, enter 200

5. Click 2 (Input Params 2)

6. For Elevation offset enter 1300

7. Click Accept

To configure the TPREFN module

1. Double-click the TPREFN module.

This module automatically refines tie points by eliminating those with large

residual errors.

2. For the Rejection Method, select RMS Error (5)

3. For the Minimum X RMS Error enter 10

4. For the Minimum Y RMS Error enter 10

These parameters mean that tie points with an X or Y RMS greater than 10

pixels will be rejected

5. Click Accept

To configure the first EXPORT module

1. Double-click the EXPORT module below the CPMMSEG module

2. Click Browse and navigate to the ASTER_MODELER folder

3. For the File name, enter aster_tp.prj and click Save

This will export the OrthoEngine project file

4. Click Accept

Note CPMMSEG computes and copies the math model

contained in a project file to a Math Model layer in the

input file. There are no parameters to configure for this

module.


1. Double-click the ORTHO module

ORTHO orthorectifies a raw image based on the math model computed by

CPMMSEG.

2. For the Output Projection, enter UTM 34 T D000



3. For Output Pixel Ground Size: X enter 15

4. For Output Pixel Ground Size: Y enter 15

5. Click Accept

To set the batch parameters for the EXPORT module

1. Double-click the EXPORT module to the right of the ORTHO module

2. Click Batch

3. In the Batch parameter sets table, right-click the File column heading, and

choose From Input Module

4. Click OK on the From Input Module - ORTHO window

The Batch parameter sets table updates with the names of the input files

5. Press the SHIFT key, and select both parameter sets


choose Add Prefix/Suffix

The Add Prefix/Suffix window opens

7. In the Add Prefix/Suffix window, click the Prefix Text check box and enter

ortho_

This option applies to both selected cells.

Figure 113. Add Prefix/Suffix window

8. Click OK

The File names update in the Batch parameter sets table.

9. Click Accept

To execute the model in batch mode

3. From the Execute menu, select Run Batch or click the Run Batch button on

the toolbar It will take a few minutes for the model to run to completion.

When the model has finished executing, double-click the Log tab of the TPREFN

MCP. You will see a report of the deleted tie points, the original number of tie points as well as RMS errors. The output ortho images can be viewed in Focus.

To save the model

1. From the File menu, select Save Model


2. Navigate to the ASTER_MODELER folder

3. In the File Name box, type autotie_aster.mod and click Save

The File Selector window closes and your model is saved as a MOD file



Lesson summary

In this lesson you:

Added modules to the canvas

Configured the modules for batch execution


Ran the model in batch execution model



Lesson 6.3 - Automatic GCP collection and mosaicking In this lesson you will:

Add modules to the canvas


Configure the modules for batch execution

Run the model in batch execution mode

About this lesson

Entire work flows can be set up within OrthoEngine to perform automatic image-to-

image registration, calculate the sensor model, orthorectify imagery and perform automatic mosaicking.

The model for this lesson uses a total of eight modules from the Module Librarian.

First, you will batch import two SPOT scenes. Two other IMPORT modules will be

used for a geocoded image and a DEM. GCPs will be automatically collected from

the geocoded image with AUTOGCP and then refined with GCPREFN. The satellite

model will be applied and the images will be orthorectified with ORTHO. The two

orthorectified images will be automatically mosaicked with AUTOMOS. The

individual ortho images and the mosaicked image will be exported.

To start setting up the model


3 IMPORT modules

1 AUTOGCP module

1 GCPREFN module

1 SATMODEL

1 ORTHO module

1 ACCUMULATE module

1 AUTOMOS module

2 EXPORT modules


Figure 114. Modules on canvas with no pipe connections



To setup the Batch Parameter Sets for the first IMPORT module

1. Double-click the upper IMPORT module

2. Click Browse and navigate to the SPOT folder

3. Select spotleft.pix and click Open

4. From the list of Available Layers, select the 8U raster layer and the orbital

segment

5. Click Batch

6. Click the + sign

7. Select the second Batch parameter set

8. Browse to the SPOT folder and select spotright.pix

Figure 115. IMPORT module control panel

9. Click Accept




1. Double-click the middle IMPORT module


3. Select SPOT_MOSAIC.PIX and click Open

4. Select the 8U raster layer

5. Click Accept

To configure the third IMPORT module

1. Double-click the lower IMPORT module


3. Select SPOTDEM.PIX and click Open

4. Select the 16S raster layer

5. Click Accept

Now that the IMPORT modules are configured, you will set up the pipes to connect the modules in the model.

To connect the IMPORT modules to the AUTOGCP module

1. Click the OutputRaster1 port from the upper IMPORT module, then click the

Input Image Layer port on the AUTOGCP module

2. Click the OutputRaster1 port from the middle IMPORT module, then click the

Reference Image Layer port on the AUTOGCP module

3. Click the OutputRaster1 port from the lower IMPORT module, then click the

Elevation Layer input port on the AUTOGCP module

Figure 116. Connections to AUTOGCP

To connect GCPREFN and SATMODEL

1. Connect the GCP port from the AUTOGCP to the GCPREFN module

2. Connect the Orbital port from the upper IMPORT module to the GCPREFN

module

3. Connect the GCP port from the GCPREFN to the input GCP port on

SATMODEL



4. Click the OutputORB1 port from the upper IMPORT module, then click the

Orbit Layer input port on the SATMODEL module

5. Click the OutputRaster1 port from the upper IMPORT module, then click the

Image Layers to be Processed port on the SATMODEL module

Figure 117. Connections to GCPREFN and SATMODEL

To connect ORTHO and ACCUMULATE

1. Connect the raster layer port from the upper IMPORT module to the Image

Layers to be Processed port on ORTHO

2. Connect the raster layer port from the lowermost IMPORT module to the

Elevation Layer port on ORTHO

3. Connect the Math Model port from SATMODEL to the input math model port

on ORTHO

4. Connect the Output Raster Layer port from ORTHO to the input port on the

ACCUMULATE module

To connect EXPORT and AUTOMOS

1. Connect the output raster port from ORTHO to the bottom EXPORT module

2. Connect the output port from the ACCUMULATE module to the Image Layers

to be Processed port on AUTOMOS

3. Connect the Output Mosaic Layers port on the AUTOMOS module to the top

EXPORT module

Note You can rotate the ports on a module by clicking the

Rotate button on the Modeler toolbar. This is useful to

help organize your pipes.

You will now configure the remaining modules.

To configure the AUTOGCP module



1. Double-click the AUTOGCP module.

This module automatically collects GCP to be used in the rest of this project

2. Set the Search source method as SUSAN

3. Leave the rest as defaults and click Accept

To configure the GCPREFN module

1. Double-click the GCPREFN module.

This module refines GCPs in a GCP segment and removes points with large

errors

2. Ensure the Model Type SAT (Toutin’s Model) is selected

3. For the Rejection Method, select Absolute Distance (4) from the dropdown

Mode 4 (absolute distance rejection) will be used. GCPs with residual values

greater than 1 pixel X and 1 pixel Y will be rejected

4. Click Accept


1. Double-click the ORTHO module.

ORTHO2 orthorectifies a raw image based on the math model computed by

SATMODEL.

2. For the Output Projection, enter UTM 11 E000

3. For the Output Pixel Ground Size: X, enter 10

4. For the Output Pixel Ground Size: Y, enter 10

The remaining default parameters will used

5. Click Accept

To configure the AUTOMOS module

1. Double-click the AUTOMOS module

This module performs automatic mosaicking of a set of geocoded images

2. For the Color Balancing Method, select OVERLAP

3. For the Cutline Generation Method, select EDGE

4. Click Accept

To set the batch parameters for the first EXPORT module

1. Double-click the EXPORT module to the right of the ORTHO module

2. Click Batch


choose From Input Module

The From Input Module window opens. If your model contains more than one

IMPORT module, you would select from which module to select the input file

names

4. Click OK

The Batch parameter sets table updates with the names of the input files.

5. Press the SHIFT key and select both parameter sets


choose Add Prefix/Suffix

The Add Prefix/Suffix window opens



7. In the Add Prefix/Suffix window, click the Prefix Text check box and enter

ortho_

This option applies to both selected cells.

8. Click OK

The File names update in the Batch parameter sets table.

9. Click Accept

To configure the second EXPORT module

1. Double-click the EXPORT module below the AUTOMOS module


3. Enter the file name auto_mosaic.pix and click Save

4. Click Accept

The AUTOGCP and SATMODEL modules do not need to be configured as default parameters will be used.

Figure 118. SPOT model

You are now ready to execute your model.

To execute the model

1. From the Execute menu, select Run Batch or click the Run Batch button on

the toolbar

It will take a few minutes for the model to run to completion.

To save the model

1. From the File menu, select Save Model


2. Navigate to the SPOT folder

3. In the File Name box, type spot_autogcp.mod and click Save

The File Selector window closes and your model is saved as a MOD file.



Lesson summary

In this lesson you:

Added modules to the canvas


Configured the modules for batch execution

Ran the model in batch execution model


Appendix A

Minimum GCP requirements

The following table lists the minimum number of ground control points (GCPs) to

collect, but it is recommend that you collect more than the minimum to ensure

accuracy. However, collecting over 20 GCPs per image does not significantly

improve the accuracy for most math models. To improve the accuracy, collect GCPs

evenly throughout the image at a variety of elevations and in areas where images

overlap. Also, the quality of the GCPs impacts the number needed to ensure accuracy.

Minimum GCP requirements

Math model/data Minimum GCPs Recommended

Aerial Photography 3 to 4 per project 3 per image for highest accuracy

Aerial Photography with GPS/INS

GCPs optional

Satellite Orbital:

SPOT 1 to 4 4 per image 6 to 10 per image

ASTER, AVNIR-2,

CARTOSAT, CBERS, DMC, EOC, IRS, LANDSAT, ORBVIEW, PRISM, QUICKBIRD Basic, SPOT 5

6 per image 10 to 15 per image

ASAR, EROS, ERS,

FORMOSAT-2, IKONOS, JERS, PALSAR, QUICKBIRD Ortho Ready, RADARSAT

8 per image 10 to 15 per image

ASAR/PALSAR/RADARSAT Specific Model

GCPs optional Improve accuracy with a minimum of 8 GCPs for RADARSAT only

Rational Functions:

If Computed from GCPs 5 per image * 19 per image *

If Extracted from Image File

GCPs optional For IKONOS Ortho Kit, improve accuracy with 1 or more GCPs (using zero-order

RPC adjustment); for CARTOSAT and QUICKBIRD Ortho Ready, minimum of 3 GCPs (using first-order RPC adjustment); for ORBVIEW, minimum of 1 GCP (using zero-order RPC adjustment)

Appendix A Geomatica OrthoEngine


Math model/data Minimum GCPs Recommended

Thin Plate Spline 3 per image Collecting more than the minimum will average out errors introduced by inaccurate GCPs or terrain variations

Polynomial:

First-order 4 per image Collecting more than the minimum will

average out errors introduced by inaccurate GCPs

Second-order 7 per image

Third-order 11 per image

Fourth-order 16 per image

Fifth-order 22 per image

* Depending on the number of coefficients that you want to use.

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Documents

Geomatica OrthoEngine · Introduction Geomatica OrthoEngine Paaggee G88 a iPPCCII Geeoommatticcss You should copy this data to your hard disk. Note This training manual can be used