A generalizable hierarchical Bayesian model for persistent SAR change detection

This paper proposes a hierarchical Bayesian model for multiple-pass, multiple antenna synthetic aperture radar (SAR) systems with the goal of adaptive change detection. We model the SAR phenomenology directly, including antenna and spatial dependencies, speckle and specular noise, and stationary clutter. We extend previous work1 by estimating the antenna covariance matrix directly, leading to improved performance in high clutter regions. The proposed SAR model is also shown to be easily generalizable when additional prior information is available, such as locations of roads/intersections or smoothness priors on the target motion. The performance of our posterior inference algorithm is analyzed over a large set of measured SAR imagery. It is shown that the proposed algorithm provides competitive or better results to common change detection algorithms with additional benefits such as few tuning parameters and a characterization of the posterior distribution.