Synthetic aperture radar image segmentation based on multi-scale Bayesian networks

In this paper, we propose a multi-scale Bayesian networks model and its inference algorithm. We use the multi-scale Bayesian networks model to segment the Synthetic Aperture Radar (SAR) image. The multi-scale Bayesian networks is constructed accordance with the multi-scale sequence of SAR images, whose MAP value is performed using the Belief Propagation (BP) algorithm and the corresponding parameter estimation is finished by the Expectation-Maximization (EM) algorithm. Experimental results demonstrate that the proposed multi-scale Bayesian networks model outperform the single-scale Bayesian network model and Markov Random Field - Intersecting Cortical Model (MRF-ICM).