an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image

ISCAS OpenIR

	an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image
	Xia Liegang; Wang Weihong; Hu Xiaodong; Luo Jiancheng
	2012
发表期刊	Cehui Xuebao/Acta Geodaetica et Cartographica Sinica
ISSN	1001-1595
卷号	41 期号:4 页码:591-596,604
摘要	Based on the characteristic of multispectral data, a new function called KSSV is designed in modifying the Gaussian kernel mapping by SSV matching technology. With this function, the feature space of multispectral images could be mapped to high dimension space. Then in the high dimension space, the old similarity measure based on Euclidean distance was replaced by SAM method. In this way, the characteristic information in multispectral images can be exploited adequately and used in many remote sensing applications effectively. At last, the method is applied to unsupervised (k-means clustering) and supervised (minimum distance, SVM) classification experiments. The results show that the classification method with KSSV measure can significantly increase the accuracy of distinguishing between different land types and reduce inconsistency in one category. So the improved method can be more effective in the classification of multi-spectral remote sensing images and achieve better accuracy.; Based on the characteristic of multispectral data, a new function called KSSV is designed in modifying the Gaussian kernel mapping by SSV matching technology. With this function, the feature space of multispectral images could be mapped to high dimension space. Then in the high dimension space, the old similarity measure based on Euclidean distance was replaced by SAM method. In this way, the characteristic information in multispectral images can be exploited adequately and used in many remote sensing applications effectively. At last, the method is applied to unsupervised (k-means clustering) and supervised (minimum distance, SVM) classification experiments. The results show that the classification method with KSSV measure can significantly increase the accuracy of distinguishing between different land types and reduce inconsistency in one category. So the improved method can be more effective in the classification of multi-spectral remote sensing images and achieve better accuracy.
收录类别	EI
关键词	Image Reconstruction Remote Sensing Support Vector Machines
部门归属	(1) Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China; (2) Software College Zhejiang University of Technology Hangzhou 310023 China; (3) Graduated University Chinese Academy of Sciences Beijing 100049 China
语种	中文
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15434
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Xia Liegang,Wang Weihong,Hu Xiaodong,et al. an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image[J]. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica,2012,41(4):591-596,604.
APA	Xia Liegang,Wang Weihong,Hu Xiaodong,&Luo Jiancheng.(2012).an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image.Cehui Xuebao/Acta Geodaetica et Cartographica Sinica,41(4),591-596,604.
MLA	Xia Liegang,et al."an improved spectral similarity measure based on kernel mapping for classification of remotely sensed image".Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 41.4(2012):591-596,604.