Buyi Zhang
Spatial residual blocks combined parallel network for hyperspectral image classification.
Zhang, Buyi; Qing, Chunmei; Xu, Xiangmin; Ren, Jinchang
Authors
Abstract
In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI classification. Firstly, the spatial residual blocks extract spatial features from rich spatial contexts information, which can be used to deal with the spatial variation of spectral signatures. Especially, the skip connection in spatial residual blocks is conducive to the backpropagation of gradients and mitigates the declining-accuracy phenomenon in the deep network. Secondly, the parallel structure is employed to extract spectral features. Spectral feature learning on parallel branches contains fewer independent connection weighs through parameter sharing. Thus, fewer parameters of the network require a lesser number of training samples. Furthermore, the feature fusion is conducted on the multi-scale features from different layers in the spectral feature learning part. Extensive experiments of three representative HSI data sets illustrate the effectiveness of the proposed network.
Citation
ZHANG, B., QING, C., XU, X. and REN, J. 2020. Spatial residual blocks combined parallel network for hyperspectral image classification. IEEE access [online], 8, pages 74513-74524. Available from: https://doi.org/10.1109/ACCESS.2020.2988553
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 6, 2020 |
Online Publication Date | Apr 17, 2020 |
Publication Date | Dec 31, 2020 |
Deposit Date | May 6, 2022 |
Publicly Available Date | Mar 29, 2024 |
Journal | IEEE Access |
Electronic ISSN | 2169-3536 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Pages | 74513-74524 |
DOI | https://doi.org/10.1109/ACCESS.2020.2988553 |
Keywords | Feature extraction; Training; Data mining; Support vector machines; Hyperspectral imaging; Clustering algorithms; Image classification |
Public URL | https://rgu-repository.worktribe.com/output/1085431 |
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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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