Abstract:

A multi-scale Bayesian network model and the associated inference algorithm, as well as a novel method of synthetic aperture radar (SAR) image segmentation based on the multi-scale Bayesian network are proposed. Firstly, the multi-scale Bayesian network model is constructed according to the multi-scale sequence of the SAR image. Then, the belief propagation (BP) algorithm, which consists of transmission of information among node in the same scale, from the fine scale to the coarse scale, and from the coarse scale to the fine scale, is presented to estimate the parameters of multi-scale Bayesian network model. Finally, the maximum a posteriori probabilities (MAP) of the finest scale hidden nodes are obtained to segment the SAR image. Experimental results show that the segmentation results based on the multi-scale Bayesian network model is better than those based on the single scale Bayesian network or the Markov random field method using the iterated conditional mode algorithm.