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Multi-stage convolutional broad learning with block diagonal constraint for hyperspectral image classification
Kong, Yi1,2; Wang, Xuesong1,2; Cheng, Yuhu1,2; Philip Chen, C. L.3,4
2021-09-01
Source PublicationRemote Sensing
ISSN2072-4292
Volume13Issue:17
Abstract

By combining the broad learning and a convolutional neural network (CNN), a block-diagonal constrained multi-stage convolutional broad learning (MSCBL-BD) method is proposed for hyperspectral image (HSI) classification. Firstly, as the linear sparse feature extracted by the conventional broad learning method cannot fully characterize the complex spatial-spectral features of HSIs, we replace the linear sparse features in the mapped feature (MF) with the features extracted by the CNN to achieve more complex nonlinear mapping. Then, in the multi-layer mapping process of the CNN, information loss occurs to a certain degree. To this end, the multi-stage convolutional features (MSCFs) extracted by the CNN are expanded to obtain the multi-stage broad features (MSBFs). MSCFs and MSBFs are further spliced to obtain multi-stage convolutional broad features (MSCBFs). Additionally, in order to enhance the mutual independence between MSCBFs, a block diagonal constraint is introduced, and MSCBFs are mapped by a block diagonal matrix, so that each feature is represented linearly only by features of the same stage. Finally, the output layer weights of MSCBL-BD and the desired block-diagonal matrix are solved by the alternating direction method of multipliers. Experimental results on three popular HSI datasets demonstrate the superiority of MSCBL-BD.

KeywordBlock Diagonal Broad Learning System Classification Convolutional Neural Network Hyperspectral Image
DOI10.3390/rs13173412
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000694505100001
Scopus ID2-s2.0-85114048362
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorCheng, Yuhu
Affiliation1.Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China
2.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
3.School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
4.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, 999078, Macao
Recommended Citation
GB/T 7714
Kong, Yi,Wang, Xuesong,Cheng, Yuhu,et al. Multi-stage convolutional broad learning with block diagonal constraint for hyperspectral image classification[J]. Remote Sensing,2021,13(17).
APA Kong, Yi,Wang, Xuesong,Cheng, Yuhu,&Philip Chen, C. L..(2021).Multi-stage convolutional broad learning with block diagonal constraint for hyperspectral image classification.Remote Sensing,13(17).
MLA Kong, Yi,et al."Multi-stage convolutional broad learning with block diagonal constraint for hyperspectral image classification".Remote Sensing 13.17(2021).
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