Variational Bayesian Wishart Mixture Model for Polarimetric Synthetic Aperture Radar Data

Abstract

This paper presents an automatic clustering algorithm for polarimetric synthetic aperture radar (PolSAR) data. Based on the Wishart mixture model which describes non-Gaussian areas more accurately, the PolSAR image is clustered by variational Bayesian expectation maximization algorithm. The incomplete gamma mixture distribution is incorporated to describe the equivalent number of looks for each cluster, which stands for the homogeneity and non-Gaussian statistics of each cluster. Then, the lower bound is computed each iteration until it reaches convergence. Meanwhile, spatial information is utilized by introducing two similarity measures. Finally, the experimental results of real measured AIRSAR dataset show that the proposed algorithm can obtain comparable clustering accuracy and determine the number of clusters automatically.