Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

被引:5
作者
Wen, Xianbin [1 ,2 ]
Zhang, Hua [1 ,2 ]
Zhang, Jianguang [1 ,2 ]
Jiao, Xu [1 ,2 ]
Wang, Lei [1 ,2 ]
机构
[1] Tianjin Univ Technol, Tianjin Key Lab Intelligence Comp & Novel Softwar, Tianjin 300191, Peoples R China
[2] Tianjin Univ Technol, Minist Educ, Key Lab Comp Vis & Syst, Tianjin 300191, Peoples R China
基金
中国国家自然科学基金;
关键词
SAR imagery; Classification; Multiscale; Hybrid EM algorithm; Genetic algorithm; SEGMENTATION;
D O I
10.1016/j.pnsc.2009.01.003
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A novel method that hybridizes genetic algorithm (GA) and expectation maximization ( EM) algorithm for the classification of synthetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive ( MAR) model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy. (C) 2009 National Natural Science Foundation of China and Chinese Academy of Sciences. Published by Elsevier Limited and Science in China Press. All rights reserved.
引用
收藏
页码:1033 / 1036
页数:4
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