1. Orfeo Toolbox
Segmentation, classification
Stéphane MAY
stephane.may@cnes.fr
orfeo-toolbox.org
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2. Segmentation
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...
orfeo-toolbox.org
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3. Monteverdi – Mean-shift
Filtering > Meanshift clustering
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4. Use case 1 : segmentation with mean-shift
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 orfeo-toolbox.org
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5. Orfeo toolbox - otbSegmentationApplication
otbSegmentationApplication
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6. otbSegmentationApplication (1/8)
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7. otbSegmentationApplication (2/8)
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8. otbSegmentationApplication (3/8)
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9. otbSegmentationApplication (4/8)
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10. otbSegmentationApplication (5/8)
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11. otbSegmentationApplication (6/8)
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12. otbSegmentationApplication (7/8)
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13. otbSegmentationApplication (8/8)
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14. Use case 2 : otbSegmentationApplication
Command line application : otbSegmentationApplication
Open ./theme2/extraitIm2_C/Im2_c_extrait.tif
Segment homogeneous areas
Save your results
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15. Monteverdi – Classification modules
Menu Learning
SVM classification
K-Means clustering
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16. Monteverdi
Menu Learning > K-means
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17. Use case 3 : unsupervised clustering with k-means
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
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18. Monteverdi
Menu Learning > SVM classification (1/3)
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19. Monteverdi
Menu Learning > SVM classification (2/3)
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20. Monteverdi
Menu Learning > SVM classification (3/3)
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21. Use case 4 : supervised classification with SVM (1/2)
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
orfeo-toolbox.org
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22. Use case 4 : supervised classification with SVM (2/2)
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
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23. Monteverdi – Feature extraction
Filtering
Feature Extraction (1/4)
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24. Monteverdi – Feature extraction
Filtering
Feature Extraction (2/4)
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25. Monteverdi – Feature extraction
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 indexes
Vegetation (NDVI, ARVI, etc), Soil, Built up, Water
Edge density
Mean shift
orfeo-toolbox.org
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26. Monteverdi – Feature extraction
Menu Filtering > Feature extraction (4/4)
Radiometric indexes
➢ Vegetation
NDVI, RVI, PVI, etc
➢ Soil
BI2
➢ Built up
ISU
orfeo-toolbox.org
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27. Use case 5 : Segment with Feature extraction
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
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28. Monteverdi
Thank you for your attention !
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