The development of the polarimetric synthetic aperture radar (PolSAR) applications has been accelerated by coming of new generation of SAR polarimetric satellites (TerraSAR-X, COSMO-SkyMed, RADARSAT-2, ALOS, etc.). The aim of this article is to extract the information content of the polarimetric SAR data. Cross products of four channels "HH, HV, VH, and VV" could be at least nine features in vector space and by applying the different class separability criterion, the impacts of each feature, for extracting different patterns, could be tested. We have chosen the large distance between classes and small distance within-class variances as our criterion to rank the features. Due to high mutual correlation between some of the features, it is preferable to combine the features which result in the lower number of features. Also the computational complexity will be decreased when we have lower number of features. Due to these advantages, our goal would be to decrease the number of features in vector space. To achieve that, a subset of ranked features consists of two to nine ranked features will be classified and the classification accuracy of different subsets will be evaluated. It is possible that some of the new features that have been added to the old subsets change the classification accuracy. Finally different feature subsets which were selected based on the various class-separability approaches will be compared. The subset that gives the highest overall accuracy would be the best representative of the nine originally features.
