基于混合Wishart模型的极化SAR图像非监督分类  

Unsupervised Classification for Polarimetric Synthetic Aperture Radar Images Based on Wishart Mixture Models

作　　者：钟能 杨文[1] 杨祥立 郭威 Zhong Neng;Yang Wen;Yang Xiangli;Guo Wei(School of Electronic Information, Wuhan University, Wuhan 430072, Chin)

机构地区：[1]武汉大学电子信息学院,武汉430072

出　　处：《雷达学报（中英文）》2017年第5期533-540,共8页Journal of Radars

基　　金：国家自然科学基金(61271401;61331016)~~

摘　　要：极化合成孔径雷达图像非监督分类是极化SAR图像自动化解译的重要步骤,但是在非监督分类的过程中如何确定样本类数仍然是十分具有挑战性的问题。由于像素之间具有空间相关性,因此和基于像素的分类方法相比,基于区域的分类方法能得到更加鲁棒的结果。为此,该文提出了一种基于混合Wishart模型和密度峰值聚类的区域级极化SAR图像非监督分类方法。该方法首先使用SLIC算法对极化SAR图像进行过分割,生成多个超像素区域;然后采用混合Wishart模型对超像素区域进行建模,并且利用Cauchy-Schwarz散度来衡量不同超像素区域之间的距离;最后通过密度峰值快速搜索聚类算法得到PolSAR图像的非监督分类结果。在不同极化SAR图像上的实验结果表明了该文方法的有效性。Unsupervised classification is a significant step inthe automated interpretation of Polarimetric Synthetic Aperture Radar（PolSAR） images. However, determining the number of clusters in this process is still a challenging problem. To this end, we propose a region-based unsupervised classification method for PolSAR images by introducing Wishart mixture models and a Density Peaks Clustering（DPC） algorithm. More precisely, the Simple Linear Iterative Clustering（SLIC） algorithm is first used to segment the PolSAR image into superpixels. Subsequently, the Wishart mixture models are adopted to model each superpixel, and the pairwise distances between different superpixels are measured by Cauchy-Schwarz divergence. Finally, the unsupervised classification result of the PolSAR image is obtained via clustering by fast search and find of density peaks. The experimental results obtained from different PolSAR images demonstrate that the proposed method is effective.

关 键 词：极化SAR图像 非监督分类 混合Wishart模型 密度峰值 

分 类 号：TN957[电子电信—信号与信息处理]