Some Topics in Remote Sensing Image Classification

1. Some Topics in Remote Sensing Image Classification Yu Lu2012.04.27

2. Outline • Introduction • Relevance in spatial domain • Relevance in spectral domain • Relevance among multiple features

3. Outline • Introduction • Relevance in spatial domain • Relevance in spectral domain • Relevance among multiple features

4. Introduction • Remote Sensing Image

5. Introduction • Remote Sensing Image • Multispectral image • 4-7 bands • TM1 0.45~0.52μm 蓝绿波段 • TM2 0.52~0.60μm 绿红波段 • TM3 0.63~0.69μm 红波段 • TM4 0.76~0.90μm 近红外波段 • TM5 1.55~1.75μm 近红外波段 • TM6 10.4~12.5μm 热红外波段 • TM7 2.08~2.35μm 近红外波段 • Hyperspectral image • Several hundreds of bands

6. Introduction • Remote Sensing Image Classification • Pixel labeling • Semantic image segmentation • Object class segmentation • Standard data set • One image with some pixels labeled, instead of a image database including multiple images

7. Introduction • Indian Pines 92AV3C • 0.4m~2.5m, 220 bands, 17 classes, 145*145 • Background, Alfalfa corn-notill, corn-min grass/pasture, grass/trees, grass/pasutre-mowed, Hay-windrowed, oat, wheat, woods, soybeans-notill, soybeans-min, soybean-clean, Bldg-Grass-Tree-Drives, stone-steel towers

8. Introduction • Indian Pines 92AV3C band 50 band 50 band 100 band 150 band 220 band 200

9. Introduction • Flight line C1 • 0.4m~1.0m, 12 bands • 10 classes, 949*220 • Alfalfa, Br Soil, Corn, Oats, Red Cl, Rye, Soybeans, Water, Wheat, Wheat-2

10. Introduction • Flight line C1 b a n d 1 b a n d 3 b a n d 12

11. Outline • Introduction • Relevance in spatial domain • Relevance in spectral domain • Relevance among multiple features

12. Relevance in spatial domain • How to capture spatial relevance • Features to capture spatial relevance • Filtered features: gabor • Statistical features: lbp sift

13. Relevance in spatial domain • How to capture spatial relevance • CRF

14. Relevance in spatial domain • Classifier to capture spatial relevance • Standard SVM [1] “A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification” TGRS 2012

15. Relevance in spatial domain • Classifier to capture spatial relevance • Spatial-Contextual SVM [1] “A Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification” TGRS 2012

16. Relevance in spatial domain • Classifier to capture spatial relevance • Spatial-Contextual SVM

17. Relevance in spatial domain • Classifier to capture spatial relevance • Spatial-Contextual SVM

18. Outline • Introduction • Relevance in spatial domain • Relevance in spectral domain • Relevance among multiple features

19. Relevance in spectral domain • Similar spectral properties

20. Relevance in spectral domain • Similar spectral properties

21. Relevance in spectral domain • BandClust • Splits bands into two disjoint contiguous subbands recursively • Splitting criterion: minimizing mutual infromation [2] “BandClust An Unsupervised Band Reduction Method for Hyperspectral Remote Sensing” LGRS 2011

22. Relevance in spectral domain • BandClust

23. Relevance in spectral domain • CRF to capture spectral domain [3] “Classification of multitemporal remote sensing data using Conditional Random Fields” PRRS 2010

24. Relevance in spectral domain • CRF to capture spectral domain [3] “Classification of multitemporal remote sensing data using Conditional Random Fields” PRRS 2010

25. Outline • Introduction • Relevance in spatial domain • Relevance in spectral domain • Relevance among multiple features

26. Relevance among multiple features • Multi-view feature extraction • Multi-view classifier • One classifier per view, weighted sum of outputs of all classifiers • One classifier per view , majority principle • Concatenate all features

27. Relevance among multiple features • Multi-view classifier • One classifier per view, weighted sum of outputs of all classifiers

28. Relevance among multiple features • Multi-view classifier • One classifier per view, weighted sum of outputs of all classifiers

29. Relevance among multiple features • Experiment results

30. Relevance among multiple features • Experiment results

31. Thank you