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《Chinese Journal of Radio Science》 2004-04
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Feature selection for fusion classification of multisource remotesensing images on set-valued features

SUN Liang 1,2 HAN Chong-zhao 1 KANG Xin 1(1. School of Electronic and information Engineering, Xi′an Jiaotong University,Xi′an Shanxi 710049, China; 2. University of Information Engineering, Zhengzhou Henan 450052, China)  
A novel set-valued feature selection (SVFS) method for fusion classification of multisource remote sensing images is proposed. The Framework consists of three sections: establishment of object information system from the statistical characteristic of the training samples in the original feature space, feature selection which is performed by the attribute reduction based on the binary relation about the set-valued features, and calculation of significance for each reduced attribute set. The fusion classification experiment shows that it is an effective method based on generalized rough set for the feature selection of multisource remote sensing images.
【Fund】: 国家重点基础研究发展规划项目 (2 0 0 1CB30 94 0 3)
【CateGory Index】: TP75
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【Co-references】
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1 ZHANG Qiang~1,ZHOU Qiu-sheng~2(1.Faculty of Land and Resource Engineering,Kunming University of Science and Technology,kunming 650093,China;2.Dep.of surveying and Engineering,Heilongjiang Institute Of Technology,Haerbin 150050,China);Remote sensing image classification and study using a kind of fuzzy neural networks[J];Engineering of Surveying and Mapping;2006-05
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