Unsupervised SAR Image Segmentation Using a Hierarchical TMF Model

被引：11

作者：

Zhang, Peng ^{[1
]}

Li, Ming ^{[1
]}

Wu, Yan ^{[2
]}

Liu, Gaofeng ^{[1
]}

Chen, Hongmeng ^{[1
]}

Jia, Lu ^{[1
]}

机构：

[1] Xidian Univ, Natl Key Lab Radar Signal Proc, Xian 710071, Peoples R China

[2] Xidian Univ, Sch Elect Engn, Remote Sensing Image Proc & Computat Grp, Xian 710071, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2013年 / 10卷 / 05期

关键词：

Bayesian inference; hierarchical triplet Markov field (HTMF) model; multiclass segmentation; synthetic aperture radar (SAR) image; MULTICLASS SEGMENTATION;

D O I：

10.1109/LGRS.2012.2227295

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

The triplet Markov field (TMF) model recently proposed is suitable for tackling the nonstationary image segmentation. In this letter, we propose a hierarchical TMF (HTMF) model for unsupervised synthetic aperture radar (SAR) image segmentation. In virtue of the Bayesian inference on the quadtree, the HTMF model captures the global and local image characteristics more precisely in the bottom-up and top-down probability computations. In this way, the underlying spatial structure information is effectively propagated. To model the SAR data related to radar backscattering sources, generalized Gamma distribution is utilized. The effectiveness of the proposed HTMF model is demonstrated by application to simulated data and real SAR image segmentation.

引用

页码：971 / 975

页数：5

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