Unsupervised classification and analysis of natural scenes from polarimetric interferometric SAR data

In this paper is introduced a classification approach for polarimetric interferometric SAR data sets, based on the analysis of an interferometric (6/spl times/6) polarimetric coherency matrix properties. From the Wishart probability density function of this polarimetric representation, is defined a maximum likelihood decision rule to perform an iterative adaptive classification. Another classification scheme based on the derivation of the conditional probability of the cross-correlation between both data sets is presented.