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Ideal Regularized Composite Kernel for Hyperspectral Image Classification
Peng, Jiangtao1; Chen, Hong2; Zhou, Yicong3; Li, Luoqing1
2017-04
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume10Issue:4Pages:1563-1574
Abstract

This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral image (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of the ideal kernel according to a regularization kernel learning framework, which captures both the sample similarity and label similarity and makes the resulting kernel more appropriate for specific HSI classification tasks. With the ideal regularization, the kernel learning problem has a simple analytical solution and is very easy to implement. The ideal regularization can be used to improve and to refine state-of-the-art kernels, including spectral kernels, spatial kernels, and spectral-spatial composite kernels. The effectiveness of the proposed IRCK is validated on three benchmark hyperspectral datasets. Experimental results show the superiority of our IRCK method over the classical kernel methods and state-of-the-art HSI classification methods.

KeywordComposite Kernel (Ck) Hyperspectral Image (Hsi) Classification Ideal Kernel Regularization
DOI10.1109/JSTARS.2016.2621416
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000398948400026
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
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Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Faculty of Mathematics and Statistics, Hubei Key Laboratory of Applied Mathematics, Hubei University, Wuhan 430062, China
2.College of Science, Huazhong Agricultural University, Wuhan 430070, China
3.Department of Computer and Information Science, University of Macau, Macau 999078, China
Recommended Citation
GB/T 7714
Peng, Jiangtao,Chen, Hong,Zhou, Yicong,et al. Ideal Regularized Composite Kernel for Hyperspectral Image Classification[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(4):1563-1574.
APA Peng, Jiangtao,Chen, Hong,Zhou, Yicong,&Li, Luoqing.(2017).Ideal Regularized Composite Kernel for Hyperspectral Image Classification.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(4),1563-1574.
MLA Peng, Jiangtao,et al."Ideal Regularized Composite Kernel for Hyperspectral Image Classification".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.4(2017):1563-1574.
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