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Incorporating Local and Global Geometric Structure for Hyperspectral Image Classification
Huiwu Luo; Yuan Yan Tang; Lina Yang
2014
Conference Name2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Source PublicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2014-January
IssueJanuary
Pages4092-4096
Conference Date5-8 Oct. 2014
Conference PlaceSan Diego, CA
CountryUSA
PublisherIEEE
Abstract

The highly correlated data structure makes the computational cost of hyperspectral image (HSI) much complex. The need of effective processing and analyzing of HSI has met many difficulties and become an open topic in the community of high dimensional data analysis. Local structure has shown great efficiency in feature extraction. Yet recent progress has also demonstrated the importance of global geometric structure in discriminant analysis. Thus, both the locality and global geometric structure are critical for dimension reduction. In this paper, a novel linear supervised dimensionality reduction algorithm, called Locality and Global Geometric Structure Preserving (LGGSP) projection, is proposed for dimension reduction. LGGSP encodes not only the local discriminant information into the optimal objective functions, but also the global margin information. To be specific, two adjacent graph (viz., similarity matrix and variance matrix), are constructed to detect the local intrinsic structure, simultaneously, a graph matrix to capture the global margin of different classes. Experimental results on both benchmark data sets and the real hyperspectral image data set demonstrate the effectiveness and practicability of proposed scheme.

DOIhttps://doi.org/10.1109/SMC.2014.6974573
URLView the original
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000370963704038
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Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationFaculty of Science and Technology University of Macau, Macau, P.R. China
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Huiwu Luo,Yuan Yan Tang,Lina Yang. Incorporating Local and Global Geometric Structure for Hyperspectral Image Classification[C]:IEEE,2014:4092-4096.
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