Supervised Classification of RADARSAT-2 Polarimetric Data for Different   Land Features
  The pixel percentage belonging to the user defined area that are assigned to cluster in a confusion matrix for RADARSAT-2 over Vancouver area has been analysed for classification. In this study, supervised Wishart and Support Vector Machine (SVM) classifiers over RADARSAT-2 (RS2) fine quadpol mode Single Look Complex (SLC) product data is computed and compared. In comparison with conventional single channel or dual channel polarization, RADARSAT-2 is fully polarimetric, making it to offer better land feature contrast for classification operation. 
Abhishek Maity
cs.CV