Multi frequency polarimetric SAR data classification

Multi frequency polarimetric SAR data classification requires to consider the polarimetric information fi-om each data set simultaneously. Dual data sets are classified using an iterative k-mean algorithm based on a maximum likelihood decision ride evaluated from the Wishart polarimetric density of the 6 x 6 coherency matrix. An alternative technique is also proposed which introduces the polarimetric cross - correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. Both new multifrequency polarimetric SAR data classification methods are applied to SAR data acquired over the Nezer forest (NASA /JPL AirSAR database (1989)).