Object Based Image Analysis

Object-Based Image AnalysisDominic AlocMelanie Gaspa

Framework

DATAPREPARATION

SEGMENTATION

CLASSIFICATION

FEATUREGENERALIZATION

FINALOUTPUT

1

2

3

4

5

REDGREENBLUE

ImageImage Layer

Image Object

Image Object LevelFeature

Class

TermsAn image is a set of raster image data. An image consists of at least one image layer based on pixels. Each image layer represents a type of information.

REDGREEN

BLUE

Image Image layer

BLUE

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

An image is a set of raster image data. An image consists of at least one image layer based on pixels. Each image layer represents a type of information.

REDGREEN

BLUE

Image Image layer

BLUE

Rule SetProcess

Algorithm

Segmentation is performed by splitting the image into zoned partial areas of differing characteristics. The segments are called image objects.

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Face Example

Image Segmented Image

Segment the image by homogeneity of Red, Green and Blue mean values of RGB layers

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Face Example

Image Segmented Image

Segment the image by homogeneity of Oiliness Mean value of Oiliness Layer

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Face Example

Image Segmented Image

Segment the image by homogeneity of Wrinkle Mean value of Wrinkle Layer

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

A scene, representing an image, is segmented into image objects during the process of image analysis. Image objects are organized into image object levels.

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Entire Image

Image Object Levels

Pixel

Face

foundation

eyes

blush

Iris, pupil

Face Example

nose

lips

dark

fair nosepink

lipstickred

lipliner

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Face Example

Foundation

Eyes

Nose

Lips

Pixel

Image Object Level 1

Image

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

A feature is an attribute that represents certain information concerning objects of interest (i.e., measurements, attached data or values).

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

A class is a category of image objects. It can both be used to simply label image objects or to describe its semantic meaning. Classification is a procedure that associates image objects with an appropriate class labeled by a name and a color.

Rule SetProcess

Algorithm

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule SetProcess

Algorithm

Assign class based on RGB mean values classification.

Face Example

Level 1

Level 2

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule SetProcess

AlgorithmSegmented

ImageClassified

segmented image

Face ExampleAssign class for oily and not oily classification

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule Set

ProcessAlgorithm

Rule Set is a set of processes that is stored in the ‘Process Tree’ window.

Rule Set

Process Sequence

Single Process

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule Set

ProcessAlgorithm

Processes are the main working tools for developing rule sets.

1.Single process2.Process sequence

TermsImage

Image LayerImage Object

Image Object LevelFeature

Class

Rule Set

ProcessAlgorithm

The algorithm defines the operation the process will perform.

Process related operation Segmentation algorithms Basic Classification algorithms Advanced Classification algorithms Variables operation algorithms Reshaping algorithms Level operation algorithms Interactive operations algorithms Sample operation algorithms Image layer operation algorithms Thematic layer operation algorithms Export algorithms Workspace automation algorithms

End. Thank you.

Object-Based Image Analysis

Segmentation

Classification

Exportation

DITCH EXTRACTIONAn object-oriented approachDITCHDefinition

Ditch- A long narrow excavation designed or intended to collect and drain off surface water.(Road Watch Project: Procedures Manual for Road Construction and Maintenance Ver. 2.1, August 2008)

- An artificial open channel or waterway usually constructed parallel to the dike to drain the overflow or seepage water from the river.(DPWH Technical Standards and Guidelines for Planning and Design, March 2002)

Types of Ditches

irrigation ditch drainage ditch

roadside ditch

BACKGROUND1) Limited number of literatures

2) Available literatures are not detailed enough

3) No generic methodology (Bailly, 2007)

BACKGROUND

1) Applicable to plain area only

2) Assumed to extract ditches with at least 1m in width

Scope and Limitation

General rules 1.Begin with an end in mind.

Develop strategy!2.Dwell on the class/es that needs

to be classified. 3.Think of layers that best

segment/classify the desired class/es

What are the properties of ditch?

consider geomorphological characteristics

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Framework

DATAPREPARATION

FEATUREGENERALIZATION

FINALOUTPUT1

2

3

4

5

REDGREENBLUE

CLASSIFICATION

SEGMENTATION

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Workflow

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Workflow

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Choosing Layers

bare earth

Curvature

Digital Terrain Model

is the second derivative of a surface or the slope of the slope.

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Translate Strategy into Rule Set

1

2

1

2

3

3

4

5

6

5

6

4

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Tiled Processes of Ditch Extraction

An Object-Based Approach For Wetland Mapping Using Seath Algorithm

Wetlands are important.Wetlands are those areas that are inundated

or saturated by surface or ground water at a frequency and duration to support and under normal circumstances do support, a prevalence of vegetation typically adapted for life in saturated conditions. --Ramsar Organization

Wetland Types123456789101112131415

Highland LakeSwampsPeatlandWater Impound (Rice Terraces)MarshRiverIrrigationFishpondLakeReservoirEstuariesTidal FlatsMangrove ForestSeagrass BedsCoral Reefs

Study AreaPaitan Lake

in Cuyapo, Nueva Ecija

General rules 1.Begin with an end in mind. 2.Dwell on the class/es that needs

to be classified. 3.Think of layers that best

segment/classify the desired class/es

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Feature Selection Method

Support Vector Machine

Classification Tree Analysis

Feature Space

Optimization

Separability and

Threshold

Get the big picture

Separability and Threshold (Seath) Algorithm

𝑩=18

(𝒎1−𝒎2 )2 2𝝈1

2+𝝈22+12𝒍𝒏[𝝈1

2+𝝈22

2𝝈1𝝈2]

𝑱=𝟐 (𝟏−𝒆− 𝑩)

Separability 

Threshold

Framework

DATAPREPARATION

FEATUREGENERALIZATION

FINALOUTPUT1

2

3

4

5

SEGMENTATION

CLASSIFICATION

Flowchart on Wetland Extraction

DSM

DTM

AerialIntensi

tyIntensity -

GLCM

nDSM

Multiresolution Segmentation

Manual Classification of training samples

Selection of Object Features

Exportation of Object Statistics

Object Statistics

Feature Selection and Threshold

CTI

Classification of wetland and non-

wetland

Clean-up operation

Wetland Shapefile

Process/Performed in: ArcGIS ArcGIS, ENVI ArcgGIS, LasTools Seath eCognition

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

orthophotograph or orthoimage; an aerial photograph geometrically corrected ("orthorectified") such that the scale is uniform: the photo has the same lack of distortion as a map. Unlike an uncorrected aerial photograph, an orthophotograph can be used to measure true distances, because it is an accurate representation of the Earth's surface, having been adjusted for topographic relief, lens distortion, and camera tilt.

often used as a generic term for DSMs and DTMs, only representing height information without any further definition about the surface

represents the earth's surface and includes all objects on it

represents the earth's surface and excludes all objects on it

the terrain is everywhere set to a standard of zero. The NDSM is accordingly generated by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM).

Orthophoto

Digital Elevation Model

Digital Surface Model

Digital Terrain Model

normalized Digital Surface Model (nDSM)

Choosing Layers

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

Choosing Layersa measure, collected for every point, of the return strength of the laser pulse that generated the point. It is based, in part, on the reflectivity of the object struck by the laser pulse.

A statistical method of examining texture that considers the spatial relationship of pixels is the gray-level co-occurrence matrix (GLCM), also known as the gray-level spatial dependence matrix. The GLCM functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and then extracting statistical measures from this matrix. 

Intensity

Gray level Co-Occurrence Matrix (GLCM)

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

DSM

Intensity

DTM

Intensity – Gray Level Co-Occurrence Measures

nDSM

Aerial Image

CTI

Processed Layers

Figure 1. Flowchart on wetland extraction

DSM

DTM

Aerial

Intensity

Intensity -

GLCM

nDSM

Multiresolution Segmentation

Manual Classification of training samples

Selection of Object Features

Exportation of Object Statistics

Object Statistics

Feature Selection and Threshold

CTI

Classification of wetland and not

wetland by Nearest Neighbor

Clean-up operation

Wetland Shapefile

Process/Performed in: ArcGIS ArcGIS, ENVI ArcgGIS, LasTools Seath eCognition

RULE SET DEVELOPMENT

Get the big picture

Choose, Process, and Import Data

Translate Strategy into Rule Set

Review Result

Refine/Expand Strategy

Ready for Export

DSM

DTM

Aerial

Intensity

Intensity -

GLCM

nDSM

Multiresolution Segmentation

Selection of Object Features

Manual Classification of training samples

Exportation of Object Statistics

Object Statistics

Feature Selection and Threshold

CTI

Classification of wetland and not

wetland

Clean-up operation

Wetland Shapefile

1

3

2

4

5

6

8

7

1

3

2 Not shown in the rule set

4

5

6

7

8

Translate Strategy into Rule Set

NOTE: Algorithms mentioned in literatures are masked into general statements. Decipher.