Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck

Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised de...
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Autor*in:	Congcong Wang [verfasserIn] Shouhang Du [verfasserIn] Wenbin Sun [verfasserIn] Deqin Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB)

Übergeordnetes Werk:	In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - IEEE, 2020, 16(2023), Seite 5849-5866
Übergeordnetes Werk:	volume:16 ; year:2023 ; pages:5849-5866

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/JSTARS.2023.3288294

Katalog-ID:	DOAJ095923357

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520			\|a Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.
650		4	\|a Change detection
650		4	\|a contrastive learning
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650		4	\|a self-supervised learning
650		4	\|a variational information bottleneck (VIB)
653		0	\|a Ocean engineering
653		0	\|a Geophysics. Cosmic physics
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700	0		\|a Wenbin Sun \|e verfasserin \|4 aut
700	0		\|a Deqin Fan \|e verfasserin \|4 aut
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allfields	10.1109/JSTARS.2023.3288294 doi (DE-627)DOAJ095923357 (DE-599)DOAJ9ec33ba80a55484da2ba8ce0f5936a87 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Congcong Wang verfasserin aut Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets. Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics Shouhang Du verfasserin aut Wenbin Sun verfasserin aut Deqin Fan verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 5849-5866 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:5849-5866 https://doi.org/10.1109/JSTARS.2023.3288294 kostenfrei https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 kostenfrei https://ieeexplore.ieee.org/document/10158495/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 5849-5866
spelling	10.1109/JSTARS.2023.3288294 doi (DE-627)DOAJ095923357 (DE-599)DOAJ9ec33ba80a55484da2ba8ce0f5936a87 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Congcong Wang verfasserin aut Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets. Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics Shouhang Du verfasserin aut Wenbin Sun verfasserin aut Deqin Fan verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 5849-5866 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:5849-5866 https://doi.org/10.1109/JSTARS.2023.3288294 kostenfrei https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 kostenfrei https://ieeexplore.ieee.org/document/10158495/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 5849-5866
allfields_unstemmed	10.1109/JSTARS.2023.3288294 doi (DE-627)DOAJ095923357 (DE-599)DOAJ9ec33ba80a55484da2ba8ce0f5936a87 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Congcong Wang verfasserin aut Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets. Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics Shouhang Du verfasserin aut Wenbin Sun verfasserin aut Deqin Fan verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 5849-5866 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:5849-5866 https://doi.org/10.1109/JSTARS.2023.3288294 kostenfrei https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 kostenfrei https://ieeexplore.ieee.org/document/10158495/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 5849-5866
allfieldsGer	10.1109/JSTARS.2023.3288294 doi (DE-627)DOAJ095923357 (DE-599)DOAJ9ec33ba80a55484da2ba8ce0f5936a87 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Congcong Wang verfasserin aut Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets. Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics Shouhang Du verfasserin aut Wenbin Sun verfasserin aut Deqin Fan verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 5849-5866 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:5849-5866 https://doi.org/10.1109/JSTARS.2023.3288294 kostenfrei https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 kostenfrei https://ieeexplore.ieee.org/document/10158495/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 5849-5866
allfieldsSound	10.1109/JSTARS.2023.3288294 doi (DE-627)DOAJ095923357 (DE-599)DOAJ9ec33ba80a55484da2ba8ce0f5936a87 DE-627 ger DE-627 rakwb eng TC1501-1800 QC801-809 Congcong Wang verfasserin aut Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets. Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics Shouhang Du verfasserin aut Wenbin Sun verfasserin aut Deqin Fan verfasserin aut In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE, 2020 16(2023), Seite 5849-5866 (DE-627)581732634 (DE-600)2457423-5 21511535 nnns volume:16 year:2023 pages:5849-5866 https://doi.org/10.1109/JSTARS.2023.3288294 kostenfrei https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 kostenfrei https://ieeexplore.ieee.org/document/10158495/ kostenfrei https://doaj.org/toc/2151-1535 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2472 GBV_ILN_2522 GBV_ILN_2965 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 5849-5866
language	English
source	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16(2023), Seite 5849-5866 volume:16 year:2023 pages:5849-5866
sourceStr	In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16(2023), Seite 5849-5866 volume:16 year:2023 pages:5849-5866
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institution	findex.gbv.de
topic_facet	Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB) Ocean engineering Geophysics. Cosmic physics
isfreeaccess_bool	true
container_title	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
authorswithroles_txt_mv	Congcong Wang @@aut@@ Shouhang Du @@aut@@ Wenbin Sun @@aut@@ Deqin Fan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	581732634
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language_de	englisch
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callnumber-first	T - Technology
author	Congcong Wang
spellingShingle	Congcong Wang misc TC1501-1800 misc QC801-809 misc Change detection misc contrastive learning misc remote sensing misc self-supervised learning misc variational information bottleneck (VIB) misc Ocean engineering misc Geophysics. Cosmic physics Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck
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topic_title	TC1501-1800 QC801-809 Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck Change detection contrastive learning remote sensing self-supervised learning variational information bottleneck (VIB)
topic	misc TC1501-1800 misc QC801-809 misc Change detection misc contrastive learning misc remote sensing misc self-supervised learning misc variational information bottleneck (VIB) misc Ocean engineering misc Geophysics. Cosmic physics
topic_unstemmed	misc TC1501-1800 misc QC801-809 misc Change detection misc contrastive learning misc remote sensing misc self-supervised learning misc variational information bottleneck (VIB) misc Ocean engineering misc Geophysics. Cosmic physics
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title	Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck
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title_full	Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck
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title_sort	self-supervised learning for high-resolution remote sensing images change detection with variational information bottleneck
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title_auth	Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck
abstract	Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.
abstractGer	Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.
abstract_unstemmed	Notable achievements have been made in remote sensing images change detection with sample-driven supervised deep learning methods. However, the requirement of the number of labeled samples is impractical for many practical applications, which is a major constraint to the development of supervised deep learning methods. Self-supervised learning using unlabeled data to construct pretext tasks for model pretraining can largely alleviate the sample dilemma faced by deep learning. And the construction of pretext task is the key to the performance of downstream task. In this work, an improved contrastive self-supervised pretext task that is more suitable for the downstream change detection is proposed. Specifically, an improved Siamese network, which is a change detection-like architecture, is trained to extract multilevel fusion features from different image pairs, both globally and locally. And on this basis, the contrastive loss between feature pairs is minimized to extract more valuable feature representation for downstream change detection. In addition, to further alleviate the problem of little priori information and much image noise in the downstream few-sample change detection, we propose to use variational information bottleneck theory to provide explicit regularization constraint for the model. Compared with other methods, our method shows better performance with stronger robustness and finer detection results in both quantitative and qualitative results of two publicly available datasets.
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title_short	Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck
url	https://doi.org/10.1109/JSTARS.2023.3288294 https://doaj.org/article/9ec33ba80a55484da2ba8ce0f5936a87 https://ieeexplore.ieee.org/document/10158495/ https://doaj.org/toc/2151-1535
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Self-Supervised Learning for High-Resolution Remote Sensing Images Change Detection With Variational Information Bottleneck

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