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Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
Zhang, Guochao1; Cao, Weijia2,3,4; Wei, Yantao1
Source PublicationApplied Sciences (Switzerland)

With the development of the hyperspectral imaging technique, hyperspectral image (HSI) classification is receiving more and more attention. However, due to high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels, it is challenging to achieve satisfactory performance for HSI classification. In order to overcome these challenges, this paper proposes a feature extraction method called spatial perception correntropy matrix (SPCM), which makes use of spatial and spectral correlation simultaneously to improve the classification accuracy and robustness. Specifically, the dimension reduction is carried out firstly. Then, the spatial perception method is designed to select the local neighbour pixels. Thus, local spectral-spatial correlation is characterized by the correntropy matrix constructed using the selected neighbourhoods. Finally, SPCM representations are fed into the support vector machine for classification. The extensive experiments carried out on three widely used data sets have revealed that the proposed SPCM performs better than several state-of-the-art methods, especially when the training set is small.

KeywordCorrentropy Matrix Feature Extraction Hyperspectral Image Classification Spatial Perception Spectral-spatial Feature
URLView the original
Indexed BySCIE
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000824703100001
Scopus ID2-s2.0-85133862452
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorCao, Weijia; Wei, Yantao
Affiliation1.Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China
2.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
3.Department of Computer and Information Science, University of Macau, 999078, Macao
4.Yangtze Three Gorges Technology and Economy Development Co., Ltd., Beijing, 101100, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang, Guochao,Cao, Weijia,Wei, Yantao. Spatial Perception Correntropy Matrix for Hyperspectral Image Classification[J]. Applied Sciences (Switzerland),2022,12(13).
APA Zhang, Guochao,Cao, Weijia,&Wei, Yantao.(2022).Spatial Perception Correntropy Matrix for Hyperspectral Image Classification.Applied Sciences (Switzerland),12(13).
MLA Zhang, Guochao,et al."Spatial Perception Correntropy Matrix for Hyperspectral Image Classification".Applied Sciences (Switzerland) 12.13(2022).
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