A K-Wishart Markov random field model for clustering of polarimetric SAR imagery

Abstract

A clustering method that combines an advanced statistical distribution with spatial contextual information is proposed for multilook polarimetric synthetic aperture radar (PolSAR) data. It is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the expectation maximization (EM) algorithm. A new formulation of EM is developed to jointly address parameter estimation in the K-Wishart distribution and the spatial context model, and also minimization of the energy function. Experiments are presented with simulated and real quad-pol L-band data.