%0 Journal Article
%T Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation<br>基于上下文和隐类属的小波域马尔可夫随机场SAR图像分割
%A Zhang Qiang
%A Wu Yan
%A <br>张 强
%A 吴 艳
%J 电子与信息学报
%D 2008
%I 
%X Because of the property that Synthetic Aperture Radar (SAR) images include plenty of multiplicative speckle noise, an effective image segmentation algorithm is proposed based on the wavelet hidden-class-label Markov Random Field (MRF) to suppress the affect of speckle. To consider the clustering and persistence of wavelet, the hidden-class-label MRF is extended to the wavelet domain with a new wavelet model for segmented image named hidden-class-label mixture heavy-tailed model, and interscale transition probability is estimated with improved context, then a new Maximum A Posteriori (MAP) classification is obtained. The experimental results show that the method suppresses the affect of noise effectively to achieve exact and robust segmentation result.
%K SAR image segmentation
%K Multiscale segmentation
%K Wavelet mixture heavy-tailed model
%K Hidden-class-label MRF
%K Context model<br>SAR图像分割
%K 多尺度分割
%K 小波域混合长拖尾模型
%K 隐类属马尔可夫随机场
%K 上下文模型
%K 基于上下文
%K 类属
%K 小波域模型
%K 马尔可夫随机场
%K 图像分割算法
%K SAR
%K Image
%K Segmentation
%K Label
%K Class
%K Hidden
%K Context
%K Based
%K Markov
%K Random
%K Field
%K 仿真结果
%K 鲁棒性能
%K 公式
%K Maximum
%K 最大后验
%K 转移概率
%K 尺度
%K 估计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=ED67FF580D09EB97F1DEFDB0CD167CF3&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=CA4FD0336C81A37A&sid=CC0ECB9C52F1B85F&eid=1B64850025D0BBBE&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=1&reference_num=9