Some Topics in Remote Sensing Image Classification Yu Lu Outline Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features Outline Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features Introduction Remote Sensing Image Introduction Remote Sensing Image Multispectral image 4-7 bands TM10.45~0.52m TM20.52~0.60m TM30.63~0.69m TM40.76~0.90m TM51.55~1.75m TM610.4~12.5m TM72.08~2.35m Hyperspectral image Several hundreds of bands 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 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 Introduction Indian Pines 92AV3C band 50 band 100 band 50 band 150 band 200 band 220 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 Introduction Flight line C1 band1 band1 band3 band3 b a n d 12 Outline Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features Relevance in spatial domain How to capture spatial relevance Features to capture spatial relevance Filtered features: gabor Statistical features: lbp sift Relevance in spatial domain How to capture spatial relevance CRF Relevance in spatial domain Classifier to capture spatial relevance Standard SVM [1] A SpatialContextual Support Vector Machine for Remotely Sensed Image Classification TGRS 2012 Relevance in spatial domain Classifier to capture spatial relevance Spatial-Contextual SVM [1] A SpatialContextual Support Vector Machine for Remotely Sensed Image Classification TGRS 2012 Relevance in spatial domain Classifier to capture spatial relevance Spatial-Contextual SVM Relevance in spatial domain Classifier to capture spatial relevance Spatial-Contextual SVM Outline Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features Relevance in spectral domain Similar spectral properties Relevance in spectral domain Similar spectral properties 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 BandClust Relevance in spectral domain CRF to capture spectral domain [3] Classification of multitemporal remote sensing data using Conditional Random Fields PRRS 2010 Relevance in spectral domain CRF to capture spectral domain [3] Classification of multitemporal remote sensing data using Conditional Random Fields PRRS 2010 Relevance in spectral domain Outline Introduction Relevance in spatial domain Relevance in spectral domain Relevance among multiple features 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 Relevance among multiple features Multi-view classifier One classifier per view, weighted sum of outputs of all classifiers Relevance among multiple features Multi-view classifier One classifier per view, weighted sum of outputs of all classifiers Relevance among multiple features Experiment results Relevance among multiple features Experiment results PCAGaborlbpsiftConca tenate multiv iew1 multiv iew2 Indian ( ) ( ) ( ) ( ) ( ) ( ) ( ) Flightl ineC ( ) ( ) ( ) ( ) ( ) ( ) ( )