Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey
In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4583 |
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| author | Zhengxin Wang Longlong Zhao Jintao Meng Yu Han Xiaoli Li Ruixia Jiang Jinsong Chen Hongzhong Li |
| author_facet | Zhengxin Wang Longlong Zhao Jintao Meng Yu Han Xiaoli Li Ruixia Jiang Jinsong Chen Hongzhong Li |
| author_sort | Zhengxin Wang |
| collection | DOAJ |
| description | In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be ignored. Therefore, high-precision cloud detection is an important step in the preprocessing of optical remote sensing images. In the past decade, with the continuous progress of artificial intelligence, algorithms based on deep learning have become one of the main methods for cloud detection. The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. This review provides a comprehensive overview of cloud detection algorithms based on deep learning from the perspective of semantic segmentation, and elaborates on the research progress, advantages, and limitations of different categories in this field. In addition, this paper introduces the publicly available datasets and accuracy evaluation indicators for cloud detection, compares the accuracy of mainstream deep learning models in cloud detection, and briefly summarizes the subsequent processing steps of cloud shadow detection and removal. Finally, this paper analyzes the current challenges faced by existing deep learning-based cloud detection algorithms and the future development direction of the field. |
| format | Article |
| id | doaj-art-3efa70aa6aaa4bfabd8622d26e3b6f6f |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3efa70aa6aaa4bfabd8622d26e3b6f6f2024-12-13T16:31:20ZengMDPI AGRemote Sensing2072-42922024-12-011623458310.3390/rs16234583Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A SurveyZhengxin Wang0Longlong Zhao1Jintao Meng2Yu Han3Xiaoli Li4Ruixia Jiang5Jinsong Chen6Hongzhong Li7Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaIn optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be ignored. Therefore, high-precision cloud detection is an important step in the preprocessing of optical remote sensing images. In the past decade, with the continuous progress of artificial intelligence, algorithms based on deep learning have become one of the main methods for cloud detection. The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. This review provides a comprehensive overview of cloud detection algorithms based on deep learning from the perspective of semantic segmentation, and elaborates on the research progress, advantages, and limitations of different categories in this field. In addition, this paper introduces the publicly available datasets and accuracy evaluation indicators for cloud detection, compares the accuracy of mainstream deep learning models in cloud detection, and briefly summarizes the subsequent processing steps of cloud shadow detection and removal. Finally, this paper analyzes the current challenges faced by existing deep learning-based cloud detection algorithms and the future development direction of the field.https://www.mdpi.com/2072-4292/16/23/4583cloud detectiondeep learningsemantic segmentationoptical satellite imageryremote sensing |
| spellingShingle | Zhengxin Wang Longlong Zhao Jintao Meng Yu Han Xiaoli Li Ruixia Jiang Jinsong Chen Hongzhong Li Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey Remote Sensing cloud detection deep learning semantic segmentation optical satellite imagery remote sensing |
| title | Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey |
| title_full | Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey |
| title_fullStr | Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey |
| title_full_unstemmed | Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey |
| title_short | Deep Learning-Based Cloud Detection for Optical Remote Sensing Images: A Survey |
| title_sort | deep learning based cloud detection for optical remote sensing images a survey |
| topic | cloud detection deep learning semantic segmentation optical satellite imagery remote sensing |
| url | https://www.mdpi.com/2072-4292/16/23/4583 |
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