Dirichlet过程混合模型SAR图像分割算法  

Dirichlet process mixture model SAR image segmentation algorithm

作　　者：孙莉[1] 张艳宁[1] 田光见[1] 马苗[1] 

机构地区：[1]西北工业大学计算机学院,西安710072

出　　处：《吉林大学学报（工学版）》2009年第5期1342-1346,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(60472072);陕西省自然科学基金项目(2007D07);博士后科学基金项目(20060401009)

摘　　要：传统的合成孔径雷达(SAR)图像参数化有限模型都有其特定的物理背景或者数学假设,很难准确估计SAR图像中各地物的密度分布,为了解决这一问题,提出一种基于非参数化无限混合模型的SAR图像分割方法,该方法利用Dirichlet过程对SAR图像进行建模,进一步采用非参数化Bayes模型分割包含复杂地物目标的SAR图像。Dirichlet分布作为一种基于分布的分布可以确定不同类别的先验概率,由样本估计出密度函数来描述图像,从而可以更精确地分割各类地物。该算法在模拟图像与真实SAR图像上进行了比对测试,实验结果验证了Dirichlet过程混合模型SAR图像分割算法的有效性和稳健性。It is difficult to segment SAR image because of the speckle noise. The traditional segmentation method of finite mixture model has been adopted extensively in SAR image segmentation. However, both the performance and the robustness are not good enough. A non- parametric Bayesian infinite mixture model, called Dirichlet Process Mixture Model （DPMM） clustering method is proposed to segment SAR image. The proposed model can simulate the intrinsic property of SAR image and the segmentation method can determine the number of clusters automatically. Experiments on both simulated data and real data are carried out. The results verify the effectiveness and robustness of the proposed method in comparison with the traditional methods.

关 键 词：计算机应用 非参数化Bayes模型 SAR图像分割 无限混合模型 Dirichlet过程混合模型 

分 类 号：TN911.73[电子电信—通信与信息系统]