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  TM10.45~0.52μm 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

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 Spatial–Contextual Support Vector Machine for Remotely Sensed Image Classification” TGRS 2012

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

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 ( ) ( ) ( ) ( ) ( ) ( ) ( )