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Feature extraction based on kernel sparse representation for hyperspectral image classification
Haoliang Yuan; Huiwu Luo; Lina Yang; Yang Lu; Yulong Wang; Yuan Yan Tang
2014
Conference Name2014 IEEE International Conference on Systems, Man, and Cybernetics
Source PublicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2014-January
IssueJanuary
Pages4071-4076
Conference Date5-8 Oct. 2014
Conference PlaceSan Diego, CA
CountryUSA
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Feature extraction is a promising technique for hyperspectral image classification. Recent research has shown that the criterion of sparse representation classification (SRC) can help to design a feature extraction method. This method is called the SRC steered discriminative projection (SRCDP). Motivated by the fact that kernel trick can exploit the nonlinear case of features, this paper generalizes SRCDP to its kernel case named KSRCDP. Extensive experiments show that KSRCDP can obtain excellent classification performance on two classic hyperspectral images.

DOIhttps://doi.org/10.1109/SMC.2014.6974570
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000370963704035
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science University of Macau Macau, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Haoliang Yuan,Huiwu Luo,Lina Yang,et al. Feature extraction based on kernel sparse representation for hyperspectral image classification[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:4071-4076.
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