Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images
Cloud cover significantly decreases the quality of optical remote sensing (ORS) images, adversely impacting its effectiveness in geographic monitoring, disaster prevention, and advanced visual applications. This phenomenon has made cloud removal a critical preprocessing step in ORS image processing....
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11039671/ |
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| _version_ | 1839638918079709184 |
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| author | Jin Ning Lianbin Xie Jie Yin Yiguang Liu |
| author_facet | Jin Ning Lianbin Xie Jie Yin Yiguang Liu |
| author_sort | Jin Ning |
| collection | DOAJ |
| description | Cloud cover significantly decreases the quality of optical remote sensing (ORS) images, adversely impacting its effectiveness in geographic monitoring, disaster prevention, and advanced visual applications. This phenomenon has made cloud removal a critical preprocessing step in ORS image processing. This article comprehensively reviews cloud removal techniques and classifies them based on the type of auxiliary data used: single-image, multimodal, and multitemporal. The discussed methods include physical modeling, deep learning, multispectral analysis, and synthetic aperture radar (SAR) fusion strategies. This article analyzes the core concepts and fundamental processes of these techniques and addresses the challenges encountered in actual scenarios. The article also includes future research directions. Moreover, the article outlines the benchmark datasets and evaluation metrics commonly used in cloud removal, thereby establishing a standardized reference for algorithm development and performance evaluation. A thorough comparative analysis was performed to assess their performance variations using visualization outcomes from the most recent and representative methodologies. |
| format | Article |
| id | doaj-art-bfcfdd1bef514d0a9b13a68ade24c9b0 |
| institution | Matheson Library |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-bfcfdd1bef514d0a9b13a68ade24c9b02025-07-04T23:00:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118159141593010.1109/JSTARS.2025.358071811039671Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing ImagesJin Ning0https://orcid.org/0000-0002-8551-7038Lianbin Xie1https://orcid.org/0009-0005-1747-731XJie Yin2Yiguang Liu3https://orcid.org/0000-0002-8223-1173College of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science and Cyber Security (Pilot Software College), Chengdu University of Technology, Chengdu, ChinaCollege of Computer Science, Sichuan University, Chengdu, ChinaCloud cover significantly decreases the quality of optical remote sensing (ORS) images, adversely impacting its effectiveness in geographic monitoring, disaster prevention, and advanced visual applications. This phenomenon has made cloud removal a critical preprocessing step in ORS image processing. This article comprehensively reviews cloud removal techniques and classifies them based on the type of auxiliary data used: single-image, multimodal, and multitemporal. The discussed methods include physical modeling, deep learning, multispectral analysis, and synthetic aperture radar (SAR) fusion strategies. This article analyzes the core concepts and fundamental processes of these techniques and addresses the challenges encountered in actual scenarios. The article also includes future research directions. Moreover, the article outlines the benchmark datasets and evaluation metrics commonly used in cloud removal, thereby establishing a standardized reference for algorithm development and performance evaluation. A thorough comparative analysis was performed to assess their performance variations using visualization outcomes from the most recent and representative methodologies.https://ieeexplore.ieee.org/document/11039671/Cloud removalmultimodalmultitemporaloptical remote sensing (ORS)single-image |
| spellingShingle | Jin Ning Lianbin Xie Jie Yin Yiguang Liu Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloud removal multimodal multitemporal optical remote sensing (ORS) single-image |
| title | Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images |
| title_full | Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images |
| title_fullStr | Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images |
| title_full_unstemmed | Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images |
| title_short | Cloud Removal Advances: A Comprehensive Review and Analysis for Optical Remote Sensing Images |
| title_sort | cloud removal advances a comprehensive review and analysis for optical remote sensing images |
| topic | Cloud removal multimodal multitemporal optical remote sensing (ORS) single-image |
| url | https://ieeexplore.ieee.org/document/11039671/ |
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