A Bayesian Nonparametric Model for Unsupervised Change Detection of Fully Polarimetric SAR Images

This paper proposes a new method for automatic change detection in multilook polarimetric synthetic aperture radar (PolSAR) images based on unsupervised classification of these data. Changes are identified by comparing multiple classification images. A Bayesian Nonparametric (BNP) approach such as the Dirichlet process mixture model (DPMM) is a potential method for classification tasks. It provides a framework for estimating both the number of components in a mixture model and the parameters of the individual mixture components simultaneously from PolSAR data. Usually, DPMM is treated using Markov chain Monte Carlo (MCMC) or variational Bayes (VB) methods. Here, we propose an expectation-maximization (EM) algorithm to estimate the parameters of the DPMM for high resolution PolSAR images which are modeled by the product model. The performance of the proposed method is evaluated with PolSAR images and the preliminary results on classification and finally on change detection are satisfactory and meet our expectations.