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Leica Geosystems GIS & Mapping, LLC..

2002 Leica Geosystems

Section 4:

Feature Extraction


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2 -Leica Geosystems GIS & Mapping LLC. Fundamentals of ERDAS IMAGINE Section 4: Feature Extraction

Feature Extraction

Interested only in geographic regions or

certain features within images

Extraction and analysis of only thesefeatures can save

Processing time

System resources


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Feature Extraction

1. Subsetting/Data Reduction2. Make features more obviousImage

Enhancing

Bands and Band Combinations

Stretching

3. Identify Areas of Interest

4. Extract features into new file


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Subsetting

Reduce the quantity of information by extracting

areas of interest


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Subsetting

Inquire Box Rectangular area

Vector Coverage Numerous features

AOI Feature

Spectral Subsetting Removing Bands


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Using Spectral Characteristics


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Individual, specific bands are good forlooking at certain features

Specific band combinations are good forlooking at certain features

Visual Enhancement - Bands


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Visible Bands

Blue Mapping coastal water areas Differentiating between soil and vegetation

Forest types mapping

Cultural features

Green Healthy vegetation

Cultural features

Red Discriminating different plant species

Soil boundaries

Geological boundaries

Cultural features

Visible Infrared Thermal

0.45-0.52

0.52-0.60

0.63-0.69


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Reflective Infrared Bands

Near Infrared Vegetation biomass

Crop identification

Soil/crop and land/water contrasts

Mid- Infrared Moisture content of plants

Crop drought studies

Plant health analyses

Discriminating between clouds, snow and ice Geologic rock type and soil boundaries

Soil and vegetation moisture content


Mid IR

2.08-2.35

Mid IR

1.55-1.74

Near IR

0.76-0.90


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Thermal Bands

Vegetation/crop stress detection Heat intensity

Insecticide applications

Thermal pollution

Geothermal activity


Thermal10.04-12.35


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Band Combinations

Bands 4,3,2 TM

Appears similar to aninfrared photograph

Vegetation appears red

Water appears navy orblack

False Color True Color Pseudo Color Others


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Band Combinations

Bands 3,2,1 TM

Objects look as theywould to the naked eye

Can look like a colorphotograph



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Band Combinations

Bands 5,4,2 TM

Colors do not reflect thefeatures in naturalcolors.

Roads may be Red

Vegetation may be blue



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Band Combinations

Bands 5,4,3 TM

Urban Distinction

Vegetation Distinction

Bands 7,4,2 TM

Similar to 5,4,2 Good for urban

distinction



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Image Contrast

0 255

Frequency

0 255

Frequency

Dark

Little CONTRASTbetween features

Brighter

More CONTRASTbetween features


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Image Contrast

0 255

F

requency

Pixel Values ofraw image

Values as theycould appear indisplay

Dark Bright


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Stretching Image Histograms

0 255

Frequency

Fits the narrow rangeof raw data

0255

Frequency

into the larger range

of the display device

Achieved using aLook Up Table (LUT)

IMAGE

SCREEN


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Contrast Tools

Histogram Equalization

Linear Stretch

Standard Deviation

Tries to put equal numbers of pixelsin a set of bins.

0 255

Frequency

10 Bins255Bins

Linear stretch between a lower and aupper value

Determines standard deviationsaround the mean and stretchesacross this region. Default inIMAGINE

2 SD


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0

Contrast Stretch

Stretches the range of data from the lowervalues to the higher values so there is highercontrast when an image is displayed

Original HistogramStandard Deviation Stretch

0 255

Freqenc

s s s


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Other Contrast Stretches

Histogram Equalization

Pixels are grouped into a userspecified number of bins

Attempt to place the same

number of pixels in each bin


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Using Band Algebra


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NDVI

Normalized Difference Vegetation Index

NDVI=IR-R/IR+R

Indices are used in

vegetation analyses to

highlight small differencesbetween classes

Often enhances differences

which cannot be seen with

display of original color bands


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NDVI

Vegetated Areas generally yield high values

High reflectance in the IR, low reflectance in theVISIBLE

Clouds, water, and snow have negative

values High Visible, Low IR

Rock and bare soil give near zero values

Similar reflectance in the near IR and the VISIBLE


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NDVI

Helps accommodate for changing

illumination and conditions, surface slopeand aspect b/c the ratio is still the same inthose areas


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Band Algebra

TM Bands 1 2 3 4

%R

eflectance

0

0.4Wavelengths in microns

60

0.7

Vegetation

Dry Soil

TM Band 4: HIGH Value

TM Band 3: LOW Value

TM Band 4 TM Band 3

= HIGH VALUE

HEALTHY VEGETATION

DRY SOIL

TM Band 4: MODERATE Value


TM Band 4 TM Band 3

= LOW VALUE

TM Band 4: HIGH Value

TM Band 3: LOW Value




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Band Algebra

Normalized Differential Vegetative Index(NDVI)

Stressed vegetationModerate Values

Healthy vegetationHigh Values


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Creating and Editing Shapefiles


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Creating Shape Files

Two techniques to generate feature layers Manual digitizing using editing tools

Semi-Automated digitizing: Region Grow tool

Manual Region Grow


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g

Manual Feature Extraction

Create a new layer containing required information

Use tool palettes and on-screen digitizing

Manual Cubic

Region Grow


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Determine areas of continuous spectralresponse

1. Select a sample pixel, a

seed

g

Region Growing

Similar pixelvalues

Dissimilar values

2. Software evaluates DN

values of surrounding pixels

- Similar: included in region

- Dissimilar: not included

3. Graphic polygon drawn

around similar pixels


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Region Grow Properties

Controls which pixel values should be included in theregion

Controls the similarity. Set to0 and only pixels of samevalue as the selected pixel areincluded in region

Controls which neighboring pixelsare considered contiguous

Constrains the search region byarea or distance

Use to regrow region if anyproperties are changed

Manualb

Region Grow


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Region Growing

Spectral Euclidian Distance

104

107 8

4103108

4104112

104 106 107 5

8 7

5 7 104

5

4

6

6463108

109 111108

10210663

4 8 103

646265

5

Cubic

Neighbor determines where the softwaresearches

= 0= 1= 2= 3


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Region Grow and Reshape

Very often the region wont be exactly as required

Reshape tool used to make small adjustments

Vertices can be moved,deleted and added tobetter represent thefeature


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Feature Layers

Usually vector layers

Annotation, Vector, AOI (and Shape files)

But could also be raster layers

Vector Layers

Raster Layers


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AOI Annotation Vector


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Editing Tools - Annotation

Used to annotate imagery

Can have basic attributes More a visual extraction tool

Map Composer tools

Order tools

Alignment tools

Rotation tools

AOI Annotation Vector


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41 -Leica Geosystems GIS & Mapping LLC.

Fundamentals of ERDAS IMAGINE Section 4: Feature Extraction

Editing Tools - Vector

Most analytical of feature layers

Points, Lines and Areas can all haveattributes

Can be analyzed in IMAGINE orARC/INFO

Editing tools are more comprehensive

Use of any tool palette

icon can be found in theviewer status bar


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Identifying and Extracting Change


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Change Detection

Requires temporal datasets1987 1992

Changes automatically

identified


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Change Detection

Use the before image and an after image to

highlight areas of change between the two

Two output files:

Image Difference File

Highlight Change File


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Image Difference File

Grayscale Image

Reflects change inbrightness values overtime

Output == Before Image After Image


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Highlight Change File

Five-class thematic image

Divided into fivecategories:

Background

Decreased Some Decreased

Unchanged

Some Increase Increased


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Change Detection

BEFORE IMAGE AFTER IMAGE

IMAGE DIFFERENCE FILE HIGHLIGHTCHANGE FILE


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