1. Some Topics in Remote Sensing Image Classification Yu Lu 2012.04.27

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  TM10.45~0.52μm 蓝绿波段  TM20.52~0.60μm 绿红波段  TM30.63~0.69μm 红波段  TM40.76~0.90μm 近红外波段  TM51.55~1.75μm 近红外波段  TM610.4~12.5μm 热红外波段  TM72.08~2.35μm 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 100  band 50  band 150  band 200  band 220

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  band1 band1  band3 band3  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.  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 Relevance in spectral domain

22.  BandClust Relevance in spectral domain

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

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

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 PCAGaborlbpsiftConca tenate multiv iew1 multiv iew2 Indian 70.78 (0.195 6) 87.32 (0.215 6) 77.02 (0.322 3) 87.13 (0.258 0) 87.75 (0.529 8) 87.31 (0.234 7) 88.92 (0.218 3) Flightl ineC1 93.12 (0.175 1) 75.08 (2.160 8) 82.60 (0.832 7) 74.05 (1.055 7) 94.34 (1.261 7) 94.65 (0.332 8) 97.00 (0.250 7)