LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification

Deep learning (DL) algorithms have been demonstrated to have great potential in hyperspectral image (HSI) classification. However, most DL methods mainly focus on improving classification performance, neglecting the computational cost. In order to broaden the application scenarios of DL-based HSI cl...

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Autores principales:	Su Qiao, Xue-Mei Dong, Jiangtao Peng, Weiwei Sun
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Attention hyperspectral image (HSI) classification iterative pruning lightweight network residual double-branch Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/71468806f9ef491ab9ebcf4065cd60e0
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id	oai:doaj.org-article:71468806f9ef491ab9ebcf4065cd60e0
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spelling	oai:doaj.org-article:71468806f9ef491ab9ebcf4065cd60e02021-12-01T00:00:17ZLiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification2151-153510.1109/JSTARS.2021.3124321https://doaj.org/article/71468806f9ef491ab9ebcf4065cd60e02021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599575/https://doaj.org/toc/2151-1535Deep learning (DL) algorithms have been demonstrated to have great potential in hyperspectral image (HSI) classification. However, most DL methods mainly focus on improving classification performance, neglecting the computational cost. In order to broaden the application scenarios of DL-based HSI classification methods, it is necessary to develop lightweight and fast models to fit the deployment needs of computational-resource-limited platforms. Taking into account the efficiency and accuracy, this article designs a lightweight network architecture based on spectral and channel-wise attention modules, namely LiteSCANet, for HSI classification. It contains a residual double-branch structure, which makes the model effectively extract spectral-spatial features and achieve good performance with fast inference speed and low computational consumption (i.e., floating-point operations, number of parameters, and graphics processing unit memory usage). The experiment results on four benchmark datasets show that our proposed model achieves an excellent trade-off between efficiency and accuracy compared with the other six existing networks.Su QiaoXue-Mei DongJiangtao PengWeiwei SunIEEEarticleAttentionhyperspectral image (HSI) classificationiterative pruninglightweight networkresidual double-branchOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11655-11668 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Attention hyperspectral image (HSI) classification iterative pruning lightweight network residual double-branch Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Attention hyperspectral image (HSI) classification iterative pruning lightweight network residual double-branch Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Su Qiao Xue-Mei Dong Jiangtao Peng Weiwei Sun LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
description	Deep learning (DL) algorithms have been demonstrated to have great potential in hyperspectral image (HSI) classification. However, most DL methods mainly focus on improving classification performance, neglecting the computational cost. In order to broaden the application scenarios of DL-based HSI classification methods, it is necessary to develop lightweight and fast models to fit the deployment needs of computational-resource-limited platforms. Taking into account the efficiency and accuracy, this article designs a lightweight network architecture based on spectral and channel-wise attention modules, namely LiteSCANet, for HSI classification. It contains a residual double-branch structure, which makes the model effectively extract spectral-spatial features and achieve good performance with fast inference speed and low computational consumption (i.e., floating-point operations, number of parameters, and graphics processing unit memory usage). The experiment results on four benchmark datasets show that our proposed model achieves an excellent trade-off between efficiency and accuracy compared with the other six existing networks.
format	article
author	Su Qiao Xue-Mei Dong Jiangtao Peng Weiwei Sun
author_facet	Su Qiao Xue-Mei Dong Jiangtao Peng Weiwei Sun
author_sort	Su Qiao
title	LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
title_short	LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
title_full	LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
title_fullStr	LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
title_full_unstemmed	LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification
title_sort	litescanet: an efficient lightweight network based on spectral and channel-wise attention for hyperspectral image classification
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/71468806f9ef491ab9ebcf4065cd60e0
work_keys_str_mv	AT suqiao litescanetanefficientlightweightnetworkbasedonspectralandchannelwiseattentionforhyperspectralimageclassification AT xuemeidong litescanetanefficientlightweightnetworkbasedonspectralandchannelwiseattentionforhyperspectralimageclassification AT jiangtaopeng litescanetanefficientlightweightnetworkbasedonspectralandchannelwiseattentionforhyperspectralimageclassification AT weiweisun litescanetanefficientlightweightnetworkbasedonspectralandchannelwiseattentionforhyperspectralimageclassification
_version_	1718406207641747456

LiteSCANet: An Efficient Lightweight Network Based on Spectral and Channel-Wise Attention for Hyperspectral Image Classification

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