Abstract
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.
