Polarimetric SAR image classification using EM method and G0p Model

We propose a polarimetric SAR image classification method using the Expectation-Maximization (EM) algorithm. It is a semi-supervised algorithm with random initialization that only requires the number of clases to be identified as initial information. We apply the proposed algorithm to simulated and real Multilook Complex (MLC) polarimetric data, assuming a G_p⁰ mixture model. The classification performance is evaluated by means of the confusion matrix and the kappa index. Finally, we compare the results to those obtained by other authors via SEM (Stochastic EM) method using the same model and data set.