利用分离性测度多类支持向量机进行高光谱遥感影像分类  

Multi-Class Support Vector Machine Classifier Based on Separability Measure for Hyperspectral Remote Sensing Image Classification

作　　者：谭琨[1,2] 杜培军[1,2] 王小美[1,2] 

机构地区：[1]中国矿业大学国土环境与灾害监测国家测绘局重点实验室,徐州市大学路1号221116 [2]中国矿业大学测绘与空间信息工程研究所,徐州市大学路1号221116

出　　处：《武汉大学学报（信息科学版）》2011年第2期171-175,共5页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金资助项目(40401038);国家863计划资助项目(2007AA12Z162);高等学校博士学科点专项科研基金资助项目(20070290516);江苏省普通高校研究生科研创新计划资助项目(CX08B_112Z)

摘　　要：从支持向量机的基本理论出发,结合高光谱数据的分离性测度,提出了一种基于分离性测度的二叉树多类支持向量机分类器,并用OMIS传感器获得的高光谱遥感数据和Hyperion高光谱遥感数据进行实验,分析比较了各种多类SVM的分类精度,并和传统的光谱角制图和最小距离分类算法进行了比较。结果表明,SVM进行高光谱分类时,基于分离性测度的二叉树多支持向量机的分类精度最高。According to SVM theory and the separability measure of hyperspectral data,we put forward a novel binary tree multi-class SVM classifier based on separability between different classes,constructed different multi-class SVM classifiers and tested their accuracy by experimented the hyperspectral image with the 64 bands OMISII data and Hyperion hyperspectral data.The experimental results show that the novel binary tree classifier has the highest accuracy than the other multi-class SVM classifiers and some traditional classifiers（spectral angle mapping classification and minimum distance classification）.Use of the novel binary tree multi-class SVM classifier based on separability measure is a novel approach which improves the accuracy of hyperspectral image classification and expands the possibilities for scientific interpretation and application.

关 键 词：高光谱遥感 分离性测度 多类支持向量机 分类 

分 类 号：P237.3[天文地球—摄影测量与遥感]