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Some Topics in Remote Sensing Image Classification

Some Topics in Remote Sensing Image Classification. Yu Lu 2012.04.27. Outline. Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features. Outline. Introduction Relevance in spatial domain Relevance in spectral domain

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Some Topics in Remote Sensing Image Classification

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  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

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