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《Science of Surveying and Mapping》 2011-03
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Use of mixed pixels as training samples for hyperspectral remote sensing image classification by SVM

WANG Xiao-ling①②,DU Pei-jun①,TAN Kun①(①Institute of Surveying and Spatial Information Engineering,China University of Mining and Technology,Jiangsu Xuzhou 221116,China;②Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping,Jiangsu Xuzhou 221116,China)  
The key idea of Support Vector Machine(SVM) classification is locating an optimal separating hyper-plane and maximizing the margin between two classes.It is obvious that mixed pixels are much closer to the boundary of classes than pure pixels and much easier to locate the optimal separating hyper-plane.The paper used mixed pixels as training samples for SVM classifier in hyperspectral image classification.Experimental results showed that hyperspectral remote sensing image classification by SVM using mixed pixels was feasible,and its accuracy was similar to the accuracy derived from the use of a conventional pure pixel training set.The characteristic of the SVM classifier was demonstrated further that it has low dependence on the spatial distribution of training samples.
【Fund】: 国家863高技术研究发展计划项目(2007AA12Z162);; 教育部高校博士学科点专项基金项目(20070290516);; 国家自然科学基金项目(40401038 40871195)
【CateGory Index】: TP751
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