MRF model-based joint interrupted SAR imaging and coherent change detection via variational Bayesian inference

In this paper, we study the problem of interrupted synthetic aperture radar (SAR) imaging and coherent change detection (CCD) in the setting of gapped collections with missing pulses. Conventional interrupted SAR CCD technique is performed using a coherence estimator for paired SAR images which are reconstructed by matched-filtering (MF). However, this method suffers from substantial false alarms due to the poor resolution and high sidelobe level problems in the reconstruction. To improve image resolution and lessen the number of false alarms, sparsity-driven (SD) estimation techniques have been utilized to form SAR image pair and then apply coherence estimator to the resulting images. Actually, existing SD-based regularization SAR imaging algorithms are sensitive to the regularized parameters which are required to tune manually, leading to a serious restriction in practical application. To solve this problem, in this work, we develop an adaptive joint imaging and CCD algorithm in interrupted environments based on Bayesian framework. Our formulation utilizes a partially coherent model to incorporate prior information about the scenes and properties of changes. In particular, we model the sparsity and spatial clustering in changes by virtue of a Markov random fields (MRF) prior. To tackle the difficulty of the calculation of posterior, the mean-field variational Bayesian expectation-maximization (VBEM) method is utilized to simultaneously estimate the MRF parameters and the latent variables. Experimental results are provided to verify the effectiveness of the proposed method.