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Local Correntropy Matrix Representation for Hyperspectral Image Classification
Zhang, Xinyu1,2; Wei, Yantao1; Cao, Weijia3,4,5,6; Yao, Huang1; Peng, Jiangtao7; Zhou, Yicong4
2022-05
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume60
Abstract

The hyperspectral images (HSIs) classification technique has received widespread attention in the field of remote sensing. However, how to achieve satisfactory classification performance in the presence of a large amount of noise is still a problem worthy of consideration. In this article, a local correntropy matrix (LCEM)-based spatial-spectral feature representation method is proposed for HSI classification. Motivated by the successful application of information-theoretic learning (ITL), we propose to adopt correntropy matrix to represent the spatial-spectral features of HSI. Specifically, the dimension reduction is first performed on the original hyperspectral data. Then, for each pixel, we select its local neighbors within a sliding window using cosine distance for the construction of the LCEM. In this way, each pixel can be characterized as an LCEM. Finally, all the correntropy matrices are fed into a support vector machine (SVM) for final classification. In addition, we also propose a novel way to determine the size of the local window based on standard deviation. Because the LCEM as the feature descriptor can characterize discriminative spatial-spectral features, the proposed method has shown great interclass separability and intraclass compactness. Compared with other advanced approaches, the proposed LCEM method has achieved competitive performance in both evaluation indexes and visual effects, especially when the training size is very small.

KeywordCorrentropy Matrix Feature Extraction Hyperspectral Image (Hsi) Classification
DOI10.1109/TGRS.2022.3162100
URLView the original
Indexed BySCIE
Language英語English
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:000783579800048
Scopus ID2-s2.0-85128850143
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorCao, Weijia; Yao, Huang
Affiliation1.Hubei Research Center for Educational Informationization, Faulty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
2.School of Artificial Intelligence, Xidian University, Xi'an, 710071, China
3.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
4.Faculty of Science and Technology, University of Macau, Macao
5.Yangtze Three Gorges Technology and Economy Development Company Ltd., Beijing, 101100, China
6.Zhongke Langfang Institute of Spatial Information Applications, Hebei, Langfang, 065001, China
7.Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Hubei, Wuhan, 430062, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhang, Xinyu,Wei, Yantao,Cao, Weijia,et al. Local Correntropy Matrix Representation for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60.
APA Zhang, Xinyu,Wei, Yantao,Cao, Weijia,Yao, Huang,Peng, Jiangtao,&Zhou, Yicong.(2022).Local Correntropy Matrix Representation for Hyperspectral Image Classification.IEEE Transactions on Geoscience and Remote Sensing,60.
MLA Zhang, Xinyu,et al."Local Correntropy Matrix Representation for Hyperspectral Image Classification".IEEE Transactions on Geoscience and Remote Sensing 60(2022).
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