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Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification
Peng J.1,2,3; Zhou Y.3; Chen C.L.P.3
2015
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN1962892
Volume53Issue:9Pages:4810
Abstract

This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial-spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods. © 1980-2012 IEEE.

KeywordComposite Kernel Hyperspectral Image (Hsi) Classification Region Kernel Support Vector Machine (Svm)
DOIhttp://doi.org/10.1109/TGRS.2015.2410991
URLView the original
Indexed BySCI
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000356159000007
The Source to ArticleScopus
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Cited Times [WOS]:52   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPeng J.; Zhou Y.; Chen C.L.P.
Affiliation1.Hubei Univ, Fac Math & Stat, Wuhan 430062, Peoples R China
2.Hubei Univ, Hubei Prov Key Lab Appl Math, Wuhan 430062, Peoples R China
3.Univ Macau, Dept Comp & Informat Sci, Macau 999078, Peoples R China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Peng J.,Zhou Y.,Chen C.L.P.. Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2015,53(9):4810.
APA Peng J.,Zhou Y.,&Chen C.L.P..(2015).Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification.IEEE Transactions on Geoscience and Remote Sensing,53(9),4810.
MLA Peng J.,et al."Region-Kernel-Based Support Vector Machines for Hyperspectral Image Classification".IEEE Transactions on Geoscience and Remote Sensing 53.9(2015):4810.
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