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Orfeo Toolbox Segmentation, classification

Orfeo Toolbox Segmentation, classification

Stéphane MAY

stephane.may@cnes.fr

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Definition of segmentation

Extract the outlines of different regions in the image

Divide the image the image into regions with pixels which

have something in common

OTB – Monteverdi

Meanshift segmentation module

OTB (integration of ITK library)

Watershed segmentation

Region growing segmentation

Level set segmentation

Hybrid segmentation, etc...

Segmentation Segmentation

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Filtering > Meanshift clustering

Monteverdi – Mean-shiftMonteverdi – Mean-shift

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Menu File > Open

./theme2/extraitIm2_C/Im2_c_extrait.tif

Menu Filtering > Mean-shift clustering

Change radius : 5

Spectral radius : 15

Min region size : 15

Clusters : ON

Change values and Click on Run button

Click on Close button after selecting right set of parameters

See also :

➢ Image filtered / Image clustered

➢See OTB-Software-Guide.pdf for details

Use case 1 : segmentation with mean-shiftUse case 1 : segmentation with mean-shift

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otbSegmentationApplication

Orfeo toolbox - otbSegmentationApplicationOrfeo toolbox - otbSegmentationApplication

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otbSegmentationApplication (1/8)

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otbSegmentationApplication (2/8)

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otbSegmentationApplication (3/8)

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otbSegmentationApplication (4/8)

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otbSegmentationApplication (5/8)

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otbSegmentationApplication (6/8)

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otbSegmentationApplication (7/8)

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otbSegmentationApplication (8/8)

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Command line application : otbSegmentationApplication

Open ./theme2/extraitIm2_C/Im2_c_extrait.tif

Segment homogeneous areas

Save your results

Use case 2 : otbSegmentationApplicationUse case 2 : otbSegmentationApplication

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Menu Learning

SVM classification

K-Means clustering

Monteverdi – Classification modulesMonteverdi – Classification modules

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Menu Learning > K-means

MonteverdiMonteverdi

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Menu File > Open

./theme2/extraitIm2_C/Im2_c_extrait.tif

Menu Learning > k-means clustering (doc OTBSoftwareGuide.pdf)

Training 15%

Number of classes : 5

Iteration number : 100

Convergence : 0.0001

Save your results

Try with several parameters set

Visualization > Viewer > Compare results

Use case 3 : unsupervised clustering with k-meansUse case 3 : unsupervised clustering with k-means

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Menu Learning > SVM classification (1/3)

MonteverdiMonteverdi

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Menu Learning > SVM classification (2/3)

MonteverdiMonteverdi

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Menu Learning > SVM classification (3/3)

MonteverdiMonteverdi

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Menu File > Open

./theme2/IM2/extraitIm2_C/Im2_c_extrait.tif

Menu Learning > SVM Classification

Create several classes (4-5)

➢Add

➢Select polygons (right click to end a polygon)

➢Edit names

➢Change colors

Learn

Display

Use case 4 : supervised classification with SVM (1/2)Use case 4 : supervised classification with SVM (1/2)

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Menu Learning > SVM Classification

Deselect random validation set

Select Display validation

Select your classes 1 by 1

➢Select polygons (right click to end a polygon)

Display

Validate

File > Export selected polygons

Use case 4 : supervised classification with SVM (2/2)Use case 4 : supervised classification with SVM (2/2)

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Filtering

Feature Extraction (1/4)

Monteverdi – Feature extractionMonteverdi – Feature extraction

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Filtering

Feature Extraction (2/4)

Monteverdi – Feature extractionMonteverdi – Feature extraction

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Menu Filtering > Feature Extraction (3/4)

Mean, variance, Gradient, spectral angle

Original data (=> no need to concatenate channels after filtering)

Textures (energy, entropy, contrast, etc)

Morphological filters

Radiometric indexesVegetation (NDVI, ARVI, etc), Soil, Built up, Water

Edge density

Mean shift

Monteverdi – Feature extractionMonteverdi – Feature extraction

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Menu Filtering > Feature extraction (4/4)

Radiometric indexes

➢Vegetation

NDVI, RVI, PVI, etc

➢Soil

BI2

➢Built up

ISU

Monteverdi – Feature extractionMonteverdi – Feature extraction

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Menu File > Open

./theme2/IM2/extraitIm2_C/Im2_c_extrait.tif

Menu Filtering > Feature extraction

Test the following features (See OTB-Software-Guide.pdf for technical

details on algorithms)

➢Original data (=> no need to concatenate channels after filtering)

➢Spectral angle : choose one vegetation pixel

➢Variance, mean

➢NDVI

➢Meanshift filtering, etc.

Menu Learning > K-Means

Menu Learning > SVM (import polygons)

Compare your results

Use case 5 : Segment with Feature extraction Use case 5 : Segment with Feature extraction

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Thank you for your attention !

Monteverdi Monteverdi