Fast segmentation approach for SAR image based on simple Markov random field  

作　　者：Xiaogang Lei Ying Li Na Zhao Yanning Zhang 

机构地区：[1]Department of Computer Science, Northwestern Polytechnical University, Xi'an 710072, E R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第1期31-36,共6页系统工程与电子技术（英文版）

基　　金：supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013);the Natural Science Foundation of Shaanxi Province (2006F05);the Aeronautical Science Foundation (05I53076)

摘　　要：Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.

关 键 词：SAR image segmentation simple Markov random field coarse segmentation maximum a posterior iterated condition mode. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]