基于黎曼流形的极化SAR图像分类  

Classification of Polarimetric SAR Images Based on the Riemannian Manifold

作　　者：杨文[1] 钟能 严天恒 杨祥立 Yang Wen;Zhong Neng;Yan Tianheng;Yang Xiangli(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433-441,共9页Journal of Radars

基　　金：国家自然科学基金(61331016;61271401)~~

摘　　要：分类是极化SAR图像解译的核心内容之一。一种新的思路是通过利用极化SAR协方差矩阵所形成的黎曼流形结构特性进行极化SAR图像分类。该文首先回顾了极化SAR图像分析中常用的黎曼流形测度,然后论述了如何对黎曼流形上的极化协方差矩阵进行稀疏编码。在监督分类方面,基于核空间黎曼流形稀疏编码提出了融合空间信息的极化SAR图像监督分类方法;在非监督分类方面,基于黎曼稀疏编码提出了利用黎曼稀疏诱导相似度的极化SAR图像非监督分类方法。在EMISAR和AIRSAR极化数据上的实验结果表明了该文所提方法的有效性。Classification is one of the core components in the interpretation of Polarimetric Synthetic Aperture Radar（PolSAR） images. A new PolSAR image classification approach employs the structural properties of the Riemannian manifold formed by PolSAR covariance matrices. In this paper, we first review the Riemannian manifold metrics generally used in PolSAR image analysis. Then, we describe a sparse coding method for the covariance matrices in the Riemannian manifold. For supervised classification, we propose a PolSAR image classification method that considers spatial information based on kernel space sparse coding. As for unsupervised PolSAR image classification, a method that takes advantage of Riemannian sparse induced similarity is proposed. Experimental results on EMISAR and AIRSAR data demonstrate the effectiveness of the proposed methods.

关 键 词：极化SAR 图像分类 黎曼流形 稀疏编码 

分 类 号：TN957[电子电信—信号与信息处理]