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Spatial residual blocks combined parallel network for hyperspectral image classification.

Zhang, Buyi; Qing, Chunmei; Xu, Xiangmin; Ren, Jinchang

Authors

Buyi Zhang

Chunmei Qing

Xiangmin Xu



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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ZHANG 2020 Spatial residual blocks (VOR) (3.1 Mb)
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 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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