Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two b...
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Autor*in:	Rui Li [verfasserIn] Shunyi Zheng [verfasserIn] Chenxi Duan [verfasserIn] Yang Yang [verfasserIn] Xiqi Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 3, p 582
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:3, p 582

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12030582

Katalog-ID:	DOAJ014138220

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520			\|a In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.
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allfields	10.3390/rs12030582 doi (DE-627)DOAJ014138220 (DE-599)DOAJ7f0026ff8bb047fcb62f26622e62a838 DE-627 ger DE-627 rakwb eng Rui Li verfasserin aut Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q Shunyi Zheng verfasserin aut Chenxi Duan verfasserin aut Yang Yang verfasserin aut Xiqi Wang verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 3, p 582 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:3, p 582 https://doi.org/10.3390/rs12030582 kostenfrei https://doaj.org/article/7f0026ff8bb047fcb62f26622e62a838 kostenfrei https://www.mdpi.com/2072-4292/12/3/582 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 3, p 582
spelling	10.3390/rs12030582 doi (DE-627)DOAJ014138220 (DE-599)DOAJ7f0026ff8bb047fcb62f26622e62a838 DE-627 ger DE-627 rakwb eng Rui Li verfasserin aut Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q Shunyi Zheng verfasserin aut Chenxi Duan verfasserin aut Yang Yang verfasserin aut Xiqi Wang verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 3, p 582 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:3, p 582 https://doi.org/10.3390/rs12030582 kostenfrei https://doaj.org/article/7f0026ff8bb047fcb62f26622e62a838 kostenfrei https://www.mdpi.com/2072-4292/12/3/582 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 3, p 582
allfields_unstemmed	10.3390/rs12030582 doi (DE-627)DOAJ014138220 (DE-599)DOAJ7f0026ff8bb047fcb62f26622e62a838 DE-627 ger DE-627 rakwb eng Rui Li verfasserin aut Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q Shunyi Zheng verfasserin aut Chenxi Duan verfasserin aut Yang Yang verfasserin aut Xiqi Wang verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 3, p 582 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:3, p 582 https://doi.org/10.3390/rs12030582 kostenfrei https://doaj.org/article/7f0026ff8bb047fcb62f26622e62a838 kostenfrei https://www.mdpi.com/2072-4292/12/3/582 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 3, p 582
allfieldsGer	10.3390/rs12030582 doi (DE-627)DOAJ014138220 (DE-599)DOAJ7f0026ff8bb047fcb62f26622e62a838 DE-627 ger DE-627 rakwb eng Rui Li verfasserin aut Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q Shunyi Zheng verfasserin aut Chenxi Duan verfasserin aut Yang Yang verfasserin aut Xiqi Wang verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 3, p 582 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:3, p 582 https://doi.org/10.3390/rs12030582 kostenfrei https://doaj.org/article/7f0026ff8bb047fcb62f26622e62a838 kostenfrei https://www.mdpi.com/2072-4292/12/3/582 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 3, p 582
allfieldsSound	10.3390/rs12030582 doi (DE-627)DOAJ014138220 (DE-599)DOAJ7f0026ff8bb047fcb62f26622e62a838 DE-627 ger DE-627 rakwb eng Rui Li verfasserin aut Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking. hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q Shunyi Zheng verfasserin aut Chenxi Duan verfasserin aut Yang Yang verfasserin aut Xiqi Wang verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 3, p 582 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:3, p 582 https://doi.org/10.3390/rs12030582 kostenfrei https://doaj.org/article/7f0026ff8bb047fcb62f26622e62a838 kostenfrei https://www.mdpi.com/2072-4292/12/3/582 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 3, p 582
language	English
source	In Remote Sensing 12(2020), 3, p 582 volume:12 year:2020 number:3, p 582
sourceStr	In Remote Sensing 12(2020), 3, p 582 volume:12 year:2020 number:3, p 582
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Rui Li @@aut@@ Shunyi Zheng @@aut@@ Chenxi Duan @@aut@@ Yang Yang @@aut@@ Xiqi Wang @@aut@@
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author	Rui Li
spellingShingle	Rui Li misc hyperspectral image classification misc deep learning misc channel-wise attention mechanism misc spatial-wise attention mechanism misc Science misc Q Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network
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topic_title	Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network hyperspectral image classification deep learning channel-wise attention mechanism spatial-wise attention mechanism
topic	misc hyperspectral image classification misc deep learning misc channel-wise attention mechanism misc spatial-wise attention mechanism misc Science misc Q
topic_unstemmed	misc hyperspectral image classification misc deep learning misc channel-wise attention mechanism misc spatial-wise attention mechanism misc Science misc Q
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title	Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network
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title_sort	classification of hyperspectral image based on double-branch dual-attention mechanism network
title_auth	Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network
abstract	In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.
abstractGer	In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.
abstract_unstemmed	In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.
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title_short	Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network
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Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

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