Subspace-Based Support Vector Machines for Hyperspectral Image Classification

	Subspace-Based Support Vector Machines for Hyperspectral Image Classification
	Gao, Lianru 1; Li, Jun 1; Khodadadzadeh, Mahdi 1; Plaza, Antonio 1; Zhang, Bing 1; He, Zhijian 1; Yan, Huiming 1
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2015
卷号	12 期号:2
关键词	Hyperspectral image classification multinomial logistic regression (MLR) subspace-based approaches support vector machines (SVMs)
通讯作者	Li, J (reprint author), Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China.
英文摘要	Hyperspectral image classification has been a very active area of research in recent years. It faces challenges related with the high dimensionality of the data and the limited availability of training samples. In order to address these issues, subspace-based approaches have been developed to reduce the dimensionality of the input space in order to better exploit the (limited) training samples available. An example of this strategy is a recently developed subspace-projection-based multinomial logistic regression technique able to characterize mixed pixels, which are also an important concern in the analysis of hyperspectral data. In this letter, we extend the subspace-projection-based concept to support vector machines (SVMs), a very popular technique for remote sensing image classification. For that purpose, we construct the SVM nonlinear functions using the subspaces associated to each class. The resulting approach, called SVMsub, is experimentally validated using a real hyperspectral data set collected using the National Aeronautics and Space Administration's Airborne Visible/Infrared Imaging Spectrometer. The obtained results indicate that the proposed algorithm exhibits good performance in the presence of very limited training samples.
研究领域[WOS]	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000341567600027
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38292]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.[Gao, Lianru 2.Zhang, Bing 3.Yan, Huiming] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 4.[Li, Jun 5.He, Zhijian] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China 6.[Li, Jun 7.He, Zhijian] Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Guangzhou 510275, Guangdong, Peoples R China 8.[Khodadadzadeh, Mahdi 9.Plaza, Antonio] Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain
推荐引用方式 GB/T 7714	Gao, Lianru,Li, Jun,Khodadadzadeh, Mahdi,et al. Subspace-Based Support Vector Machines for Hyperspectral Image Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2015,12(2).
APA	Gao, Lianru.,Li, Jun.,Khodadadzadeh, Mahdi.,Plaza, Antonio.,Zhang, Bing.,...&Yan, Huiming.(2015).Subspace-Based Support Vector Machines for Hyperspectral Image Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,12(2).
MLA	Gao, Lianru,et al."Subspace-Based Support Vector Machines for Hyperspectral Image Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12.2(2015).