Joint interrupted SAR imaging and coherent change detection using Markov random fields

In this paper, we propose an efficient joint imaging and coherent change detection (CCD) algorithm to cope with the issue of interrupted synthetic aperture radar (SAR) environ­ments. Information about the changing scenes is considered using a partially coherent model, where the structure characteristics of changes are modeled by a Markov random fields (MRF) prior. Then the variational Bayesian expectation-maximization (VBEM) algorithm is employed to simultaneously approximate the posterior distributions of the change map and scene estimates. The proposed scheme has a superior change detection performance over the classical coherent change detectoris. Representative simulations are conducted to demonstrate the validity of the devised algorithm.