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Proceedings Paper

SAR image segmentation using MPM and constrained stochastic relaxation
Author(s): Huiyan Zhao; Yongfeng Cao; Wen Yang
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Paper Abstract

A segmentation method using maximization of the Posterior marginals (MPM) and constrained stochastic relaxation (CSR) for SAR images is proposed. This method improves the regularity of MPM based segmentation result by introducing CSR. Multi-Level Logistic (MLL) model is used for the underlying label image to introduce regularity prior of segmentation. Gamma distribution is used for SAR intensity data. The hyper parameters of MLL model are supposed to be known a priori. This method is an iterative scheme consists of two alternating steps: to approximate the MPM estimation of the pixel class labels and to estimate gamma distribution parameters. The weight of the prior energy in goal energy function is increased slowly versus the increasing iteration times until certain number of iteration has finished. The segmentation results for synthetic and real SAR images show that the proposed method has a good performance.

Paper Details

Date Published: 3 November 2005
PDF: 6 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60432V (3 November 2005); doi: 10.1117/12.655024
Show Author Affiliations
Huiyan Zhao, Wuhan Polytechnic Univ. (China)
Yongfeng Cao, Wuhan Univ. (China)
Wen Yang, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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