Bayesian classification of multi-look polarimetric SAR images with a generalized multiplicative speckle model and adaptive a priori probabilities

In this paper, a maximum likelihood (ML) classification algorithm is proposed to classify multi-look polarimetric SAR images. This algorithm considers a generalized multiplicative speckle model in which three texture variables are assumed to affect separately three polarization channels. We derive the ML estimation of the texture parameters for each polarization channel with the conditional complex Wishart distribution of the multi-look polarimetric covariance matrix, and design the corresponding ML classifier according to the Bayesian criterion. Further, a method for adaptively producing the a priori probabilities is presented in order to improve the classification accuracy. This method utilizes the contextual information in a forward procedure, and does not need any iteration. With the NASA/JPL L band four-look polarimetric SAR data, the effectiveness of the classification algorithm is demonstrated, and the use of the adaptive a priori probabilities is shown to result in improved classifications.