A fast and robust cirrus removal method for Landsat 8/9 images

High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the...
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Autor*in:	Tao Jiang [verfasserIn] Huanfeng Shen [verfasserIn] Huifang Li [verfasserIn] Chi Zhang [verfasserIn] Liying Xu [verfasserIn] Dekun Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA

Übergeordnetes Werk:	In: International Journal of Applied Earth Observations and Geoinformation - Elsevier, 2022, 128(2024), Seite 103691-
Übergeordnetes Werk:	volume:128 ; year:2024 ; pages:103691-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jag.2024.103691

Katalog-ID:	DOAJ098699393

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allfields	10.1016/j.jag.2024.103691 doi (DE-627)DOAJ098699393 (DE-599)DOAJd0d8461f7a9241f1b5db44870568c81d DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Tao Jiang verfasserin aut A fast and robust cirrus removal method for Landsat 8/9 images 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance. High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA Physical geography Environmental sciences Huanfeng Shen verfasserin aut Huifang Li verfasserin aut Chi Zhang verfasserin aut Liying Xu verfasserin aut Dekun Lin verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103691- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103691- https://doi.org/10.1016/j.jag.2024.103691 kostenfrei https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000451 kostenfrei https://doaj.org/toc/1569-8432 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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_4700 AR 128 2024 103691-
spelling	10.1016/j.jag.2024.103691 doi (DE-627)DOAJ098699393 (DE-599)DOAJd0d8461f7a9241f1b5db44870568c81d DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Tao Jiang verfasserin aut A fast and robust cirrus removal method for Landsat 8/9 images 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance. High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA Physical geography Environmental sciences Huanfeng Shen verfasserin aut Huifang Li verfasserin aut Chi Zhang verfasserin aut Liying Xu verfasserin aut Dekun Lin verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103691- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103691- https://doi.org/10.1016/j.jag.2024.103691 kostenfrei https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000451 kostenfrei https://doaj.org/toc/1569-8432 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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_4700 AR 128 2024 103691-
allfields_unstemmed	10.1016/j.jag.2024.103691 doi (DE-627)DOAJ098699393 (DE-599)DOAJd0d8461f7a9241f1b5db44870568c81d DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Tao Jiang verfasserin aut A fast and robust cirrus removal method for Landsat 8/9 images 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance. High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA Physical geography Environmental sciences Huanfeng Shen verfasserin aut Huifang Li verfasserin aut Chi Zhang verfasserin aut Liying Xu verfasserin aut Dekun Lin verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103691- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103691- https://doi.org/10.1016/j.jag.2024.103691 kostenfrei https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000451 kostenfrei https://doaj.org/toc/1569-8432 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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_4700 AR 128 2024 103691-
allfieldsGer	10.1016/j.jag.2024.103691 doi (DE-627)DOAJ098699393 (DE-599)DOAJd0d8461f7a9241f1b5db44870568c81d DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Tao Jiang verfasserin aut A fast and robust cirrus removal method for Landsat 8/9 images 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance. High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA Physical geography Environmental sciences Huanfeng Shen verfasserin aut Huifang Li verfasserin aut Chi Zhang verfasserin aut Liying Xu verfasserin aut Dekun Lin verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103691- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103691- https://doi.org/10.1016/j.jag.2024.103691 kostenfrei https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000451 kostenfrei https://doaj.org/toc/1569-8432 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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_4700 AR 128 2024 103691-
allfieldsSound	10.1016/j.jag.2024.103691 doi (DE-627)DOAJ098699393 (DE-599)DOAJd0d8461f7a9241f1b5db44870568c81d DE-627 ger DE-627 rakwb eng GB3-5030 GE1-350 Tao Jiang verfasserin aut A fast and robust cirrus removal method for Landsat 8/9 images 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance. High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA Physical geography Environmental sciences Huanfeng Shen verfasserin aut Huifang Li verfasserin aut Chi Zhang verfasserin aut Liying Xu verfasserin aut Dekun Lin verfasserin aut In International Journal of Applied Earth Observations and Geoinformation Elsevier, 2022 128(2024), Seite 103691- (DE-627)359784119 (DE-600)2097960-5 1872826X nnns volume:128 year:2024 pages:103691- https://doi.org/10.1016/j.jag.2024.103691 kostenfrei https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d kostenfrei http://www.sciencedirect.com/science/article/pii/S1569843224000451 kostenfrei https://doaj.org/toc/1569-8432 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_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_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 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_4700 AR 128 2024 103691-
language	English
source	In International Journal of Applied Earth Observations and Geoinformation 128(2024), Seite 103691- volume:128 year:2024 pages:103691-
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callnumber-first	G - Geography, Anthropology, Recreation
author	Tao Jiang
spellingShingle	Tao Jiang misc GB3-5030 misc GE1-350 misc High-quality cirrus removal misc Cirrus parallaxes misc Cirrus parallax laws and correction misc Automatic sampling misc CUDA misc Physical geography misc Environmental sciences A fast and robust cirrus removal method for Landsat 8/9 images
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topic_title	GB3-5030 GE1-350 A fast and robust cirrus removal method for Landsat 8/9 images High-quality cirrus removal Cirrus parallaxes Cirrus parallax laws and correction Automatic sampling CUDA
topic	misc GB3-5030 misc GE1-350 misc High-quality cirrus removal misc Cirrus parallaxes misc Cirrus parallax laws and correction misc Automatic sampling misc CUDA misc Physical geography misc Environmental sciences
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title	A fast and robust cirrus removal method for Landsat 8/9 images
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title_sort	fast and robust cirrus removal method for landsat 8/9 images
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title_auth	A fast and robust cirrus removal method for Landsat 8/9 images
abstract	High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance.
abstractGer	High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance.
abstract_unstemmed	High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance.
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title_short	A fast and robust cirrus removal method for Landsat 8/9 images
url	https://doi.org/10.1016/j.jag.2024.103691 https://doaj.org/article/d0d8461f7a9241f1b5db44870568c81d http://www.sciencedirect.com/science/article/pii/S1569843224000451 https://doaj.org/toc/1569-8432
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A fast and robust cirrus removal method for Landsat 8/9 images

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