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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 354704, 9 pages
Research Article

Unsupervised SAR Image Segmentation Based on a Hierarchical TMF Model in the Discrete Wavelet Domain for Sea Area Detection

1College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2No. 92677 Unit of PLA, Dalian 116001, China
3Naval Armaments Department Military Representative Office, Shenyang 110000, China
4No. 91550 Unit of PLA, Dalian 116001, China

Received 9 August 2014; Accepted 8 October 2014; Published 4 November 2014

Academic Editor: Xiaojie Su

Copyright © 2014 Jiajing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Unsupervised synthetic aperture radar (SAR) image segmentation is a fundamental preliminary processing step required for sea area detection in military applications. The purpose of this step is to classify large image areas into different segments to assist with identification of the sea area and the ship target within the image. The recently proposed triplet Markov field (TMF) model has been successfully used for segmentation of nonstationary SAR images. This letter presents a hierarchical TMF model in the discrete wavelet domain of unsupervised SAR image segmentation for sea area detection, which we have named the wavelet hierarchical TMF (WHTMF) model. The WHTMF model can precisely capture the global and local image characteristics in the two-pass computation of posterior distribution. The multiscale likelihood and the multiscale energy function are constructed to capture the intrascale and intrascale dependencies in a random field (). To model the SAR data related to radar backscattering sources, the Gaussian distribution is utilized. The effectiveness of the proposed model for SAR image segmentation is evaluated using synthesized and real SAR data.