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Proceedings Paper

Unsupervised SAR image segmentation method based on MAP classification criterion and anisotropic diffusion smoothing
Author(s): Shu Run Tan; Xin Yi He; Bo Zhao; Xiao Yang Zhou; Zhong Jin Jiang; Tie Jun Cui
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Paper Abstract

Segmentation is of great importance in the community of synthetic aperture radar (SAR) imaging interpreting and understanding. In this paper we realize an unsupervised SAR image segmentation system based on statistical maximum a posterior (MAP) classification criterion and physical heat diffusion derived anisotropic smoothing process. Generalized mixed Gaussian distribution is applied to model the image gradation with expectation maximize (EM) method implementing the parameter estimation. A novel idea is proposed to linearly combine the Gaussian branch related posterior probabilities to fit the segmentation problem size and this endows cursory initial segmentation robust adaption to a wide range of SAR data variability. Proper use of anisotropic diffusion (AD) on the posterior probability domain can effectively remove the multiplicative speckle noise of raw data and has advantage to smooth the inner area while well preserve region edges, just as optimal ultimate segmentation process requires. A brief introduction of the method is presented along with many application considerations. The correctness and efficiency of the method have been verified by several examples.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74951J (30 October 2009); doi: 10.1117/12.832784
Show Author Affiliations
Shu Run Tan, Southeast Univ. (China)
Xin Yi He, Southeast Univ. (China)
Bo Zhao, Southeast Univ. (China)
Xiao Yang Zhou, Southeast Univ. (China)
Zhong Jin Jiang, Southeast Univ. (China)
Tie Jun Cui, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis

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