高光谱数据的相似性测度对比研究  

Comparative study on similarity measures of hyperspectral remote sensing data

作　　者：张浚哲[1] 朱文泉[1] 郑周涛[1] 潘耀忠[1] 王伶俐[1] 

机构地区：[1]北京师范大学地表过程与资源生态国家重点实验室/资源学院,北京100875

出　　处：《测绘科学》2013年第6期33-36,共4页Science of Surveying and Mapping

基　　金：国家重点基础研究发展计划项目(2011CB952001);地表过程与资源生态国家重点实验室资助项目(2010-ZY-09);中央高校基本科研业务费专项资金资助项目

摘　　要：光谱相似性测度是高光谱分类的基础,实践证明多种相似性测度的结合能更加全面地表现光谱特征。本文将反应光谱曲线形状的相关系数和反映光谱曲线幅度的欧氏距离进行有效组合,在统一的测试框架下采用Chris Elvidge植被光谱库数据和Hyperion高光谱影像,对欧氏距离、光谱角余弦、光谱信息离散度、相关系数、光谱角余弦—欧氏距离、欧氏距离—相关系数这6种相似性测度方法进行综合评价,并对每种相似性测度的适用范围进行比较;研究结果显示欧氏距离—相关系数方法的分类精度要高于其他5种方法。The essence of hyperspectral image classification is spectral similarity measure, and some researches indicate that com- binative similarity measures can broadly express spectral characteristic, but few people provide systematic evaluation on the topic. Since the correlation coefficient method has a better ability to discriminate the whole shape of spectral curve than the spectral angle co- sine method, a new method, Euclidean distance-correlation coefficient, was combined with the former in the paper. Taking spectral curve data and hyperspectral Hyperion image, six similarity measures （i. e. , Euclidean distance, spectral angle cosine, spectral infor- mation divergence, correlation coefficient, spectral angle cosine-Euclidean distance and Euclidean distance-correlation coefficient） were comprehensively evaluated under a unified testing framework. Moreover, the characters of these similarity measures were compared and contrasted. The result showed that Euclidean distance-correlation coefficient method would be more precise than other five methods in accuracy of classification

关 键 词：高光谱影像 相似性测度 可区分度 聚类 分类 欧氏距离一相关系数 

分 类 号：P237.9[天文地球—摄影测量与遥感]