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《Science of Surveying and Mapping》 2011-01
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Impacts of feature dimensionality to the support vector machine classifier for hyperspectral remote sensing image

TAN Kun①②,DU Pei-jun①②,WANG Xiao-mei①②(①Key Laboratory for Terrestrial Environment and Geohazards Monitoring,State Bureau of Surveying and Mapping,Xuzhou 221116,China;②Institute of Surveying and Geospatial Information Engineering,China University of Mining and Technology,Xuzhou 221116,China)  
According to the SVM theory and the characteristics of hyperspectral remote sensing image,the classification model which is the characteristic extraction based on SVM was constructed in the paper,and the values of the penalty parameter of the error term and Radical Basis Function kernel parameter were gained by grid researching.In order to confirm SVM to the high dimension characteristic compatibility and the reliability,the different feature extraction methods and the characteristic combinations were considered,it experimented on the hyperspectral image with the 64 bands OMIS data of Beijin Chang Pin.By experiment on the characteristic extraction algorithm of Principal Component Analysis(PCA),Minimum Noise Fraction(MNF),characteristic extraction after correlation coefficient grouping,derivative spectral and so on,it indicated that the support vector classification model fluctuated with characteristic dimension and PCA had the best precision.It was concluded that SVM classifier had more advantages under the same feature input after comparing with Maximum Likelihood classification and Spectral Angle Mapping classification model.
【Fund】: 国家自然科学基金项目(40401038);; 国家高技术研究发展计划863计划项目(2007AA12Z162);; 高等学校博士学科点专项科研基金资助课题(20070290516);; 江苏省普通高校研究生科研创新计划资助项目(CX08B_112Z);; 中央高校基本科研业务费专项资金资助(2010QNA18)
【CateGory Index】: TP751
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