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Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification
Cheng, Chunbo1; Li, Hong2; Peng, Jiangtao3; Cui, Wenjing1; Zhang, Liming4
2022-03
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume60
Abstract

Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information compared with the vector representation, which can help improve the classification performance of HSI. In this article, a deep high-order tensor convolutional sparse coding (CSC) model is proposed, which can be used to train deep high-order filters. Based on the deep high-order tensor CSC model, a deep feature extraction network (DHTCSCNet) is constructed, which is used for feature extraction of HSIs. By combining the spectral-spatial feature and the features extracted by the proposed DHTCSCNet at each layer, a combined feature that incorporates shallow, deep, spectral, and spatial features can be obtained. Then, the graph-based learning (GSL) methods are used to classify the combined feature. Experimental results show that the DHTCSCNet can obtain better classification performance compared with other HSI classification methods.

KeywordDeep High-order Tensor Convolutional Sparse Coding (Csc) Deep Learning Graph-based Learning (Gsl) Hyperspectral Image (Hsi) Classification
DOI10.1109/TGRS.2021.3134682
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:000766298800024
Scopus ID2-s2.0-85121335095
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorLi, Hong
Affiliation1.School of Mathematics and Physics, Hubei Polytechnic University, Huangshi, 435000, China
2.School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China
3.Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, 430062, China
4.Faculty of Science and Technology, University of Macau, 999078, Macao
Recommended Citation
GB/T 7714
Cheng, Chunbo,Li, Hong,Peng, Jiangtao,et al. Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60.
APA Cheng, Chunbo,Li, Hong,Peng, Jiangtao,Cui, Wenjing,&Zhang, Liming.(2022).Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification.IEEE Transactions on Geoscience and Remote Sensing,60.
MLA Cheng, Chunbo,et al."Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification".IEEE Transactions on Geoscience and Remote Sensing 60(2022).
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