利用TMF和置信传播的无监督SAR图像分割

doi:10.3969/j.issn.1001-2400.2015.06.009

Abstract

Abstract:

To solve the problem that the traditional statistical inference approach for the triplet Markov fields (TMF) model cannot balance segmentation accuracy and computational efficiency, an efficient statistical inference approach for the TMF model using belief propagation is proposed, and then applied to unsupervised synthetic aperture radar (SAR) image segmentation. The algorithm combines the TMF model and the statistical property of the SAR image, and translates the segmentation problem into maximization of the posterior marginal (MPM) estimation. For the two label fields in TMF, the belief propagation algorithm is generalized to the bivariate case to estimate the joint posterior marginal probability of the two label fields through message passing. The two label fields can be simultaneously estimated according to the MPM criterion. Experiments on both simulated and real SAR images demonstrate that the proposed algorithm can efficiently suppress the influence of the speckle, and obtain accurate segmentation results with a reasonable computational cost.

Key words: synthetic aperture radar, image segmentation, triplet Markov fields, belief propagation

GAN Lu;WU Yan;WANG Fan. Unsupervised SAR image segmentation using TMF and belief propagation[J].J4, 2015, 42(6): 49-55.

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Unsupervised SAR image segmentation using TMF and belief propagation

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