Image Classification

Digital Image Processing Techniques

Image Restoration Image Enhancement Image ClassificationImage Classification

Image Classification: the art and science of using the computer to interpret the image.

Why do it?Why do it?

Especially when automated computer methods oppose a long “proven” history of visual/manualimageinterpretation

However, with image classification you can make cool looking maps with more spatial detail than humans would ever draw!

Coop Project withCal Fish and Game15-meter Landsat7Pan Sharpened ImageryModified CWHRClassification

#Y

#Y#YLocations 6 and 7

Location 4

Post-fire ConditionsBG, bare ground & discolored foliage >70% deadVST, only vertical stems remain >90% deadVSB, vertical stems & branches remain >90% deadSHDB, shadow area, suspect burned 60-70% deadCRNS, only upper third of discolored crowns remain >90% deadBL, consumed lwr crown, upr 2/3 crown discol. >70% deadDIS, discolored crown 40-70% deadSHD, shadow area, no inference possibleSHDH, shadow area, suspect unburned <25% deadUNB, apparently unburned <25% dead vegetationMargin, outside image area

#Y Field Data Locations

1000 0 1000 2000 Feet

Represent detailed conditions on the ground

Forest Cover Classification in Cameroon

Semi-automatedchange detection

A Combination of supervised image A Combination of supervised image classification, polygon formation and classification, polygon formation and visual editing of resulting polygons visual editing of resulting polygons proves useful for forest monitoring.proves useful for forest monitoring.

Semi-AutomatedChangeDetectionBased upon5km by 5kmBlocks ofSatelliteImagery

Image to Image Registration

Accomplished withSPEAR tools

In ENVI

The multi-datestackedimageallowscreationoftwo-datecolorcompositesthat allow the visualidentificationof change

ENVI EX used to classify the image block into four classes:

Forest (unchanged)Non-Forest (unchanged)Deforestation (forest changed to non-forest)Reforestation (non-forest changed to forest)

Training areas defined for all spectral classes visiblein the image

Smooth the image before creating polygons

Area Summary Table

CLASS_NAME area percentageDeforestation 58.30 2.33Reforestation 13.66 0.55Forest 2083.93 83.27Non-forest 346.61 13.85

2502.50 100.00

Objectives:

Understand the principle of supervised Understand the principle of supervised classification including definition of classes classification including definition of classes and selection of training areasand selection of training areas

Describe the maximum likelihood Describe the maximum likelihood classification algorithm, the one most often classification algorithm, the one most often used.used.

Image Classification

SupervisedSupervised Training stage - analyst determines Training stage - analyst determines

source identitysource identity Classification stageClassification stage Output stageOutput stage

Supervised Classification

SelectTrainingAreas

Edit/ Evaluate Signatures

EvaluateClassification

Classify Image

Subjective human influence selects “representative samples” of all landcover types required for the analysis< 5% of the pixels used for training.

Subjective human judgment resolvesproblems: spectral signatures not separable, or spectral signaturesredundant.

Unbiased machine determines theclass into which the unknown pixelsare assigned (>95 % of the pixels areunknown before classification).

Again subjective humans evaluateresults and define new classes to change things as they desire.

Spectral response measurements (spectral signatures) recorded across 7 Landsat TM Spectral response measurements (spectral signatures) recorded across 7 Landsat TM bands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIRbands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIR

Classification Based on Spectral Signatures

1 2 3 4 5 6 71 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7WaterSand Forest UrbanCorn Hay

Adapted from Lillesand and Kiefer, 1999

Supervised Classification

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Classification Stagecompare unknown pixels to known spectral “signatures”

Output Stagetypically, a color-coded map

Training Stagecreate classes

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Adapted from Lillesand and Kiefer, 1999

identify training areas of uniform class land cover

assign to most similar

Traditional way to view spectral signatures.

BAND 3 BAND 4

redvisible

very near IR

waterforest

haycorn

urban sand

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Band 4 Digital Number

Supervised Classification Stage

Two-band scatter Two-band scatter diagram showing diagram showing spectral separability spectral separability of different land of different land covers covers

water

urban

hay

sand

corn

forest

1

2

3

Determine land cover class of each pixel in the scene

Adapted from Lillesand and Kiefer

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Supervised Classification -Maximum Likelihood Classifier

Gaussian probability Gaussian probability function computed function computed for each pixel for for each pixel for each classeach class

1

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Adapted from Lillesand and Kiefer

Pixel assigned to class for which its probability of membership is the greatest.

Can be limited to some number of standard deviations or probability threshold.

Classification - Error MatrixPixels as classified by

ground truthPix

els

as

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629

/689=

91

%…

9%

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Classification accuracy from producer’s view…how many of the known urban pixels were

classified by the computer as urban?629/702=90%…10% Error of Omission

Correctly classified pixels

Overall Accuracy =1033+629+385

+319+20/2578 = 93%