A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances
Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspect...
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| 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/2072-4292/16/24/4629 |
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| author | Zhuoya Ni Mengdie Wu Qifeng Lu Hongyuan Huo Chunqiang Wu Ruixia Liu Fu Wang Xiaoying Xu |
| author_facet | Zhuoya Ni Mengdie Wu Qifeng Lu Hongyuan Huo Chunqiang Wu Ruixia Liu Fu Wang Xiaoying Xu |
| author_sort | Zhuoya Ni |
| collection | DOAJ |
| description | Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection. |
| format | Article |
| id | doaj-art-708b3cf3ead84e20b69dda458bd186e0 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-708b3cf3ead84e20b69dda458bd186e02024-12-27T14:50:43ZengMDPI AGRemote Sensing2072-42922024-12-011624462910.3390/rs16244629A Review of Research on Cloud Detection Methods for Hyperspectral Infrared RadiancesZhuoya Ni0Mengdie Wu1Qifeng Lu2Hongyuan Huo3Chunqiang Wu4Ruixia Liu5Fu Wang6Xiaoying Xu7CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaSchool of Earth Science and Resources, China University of Geosciences, Beijing 100083, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaFaculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaCMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, ChinaChinese Academy of Meteorological Sciences, Beijing 100081, ChinaCloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection.https://www.mdpi.com/2072-4292/16/24/4629hyperspectral infrared soundercloud detectioncloud-clearingnumerical weather predictiondeep learning |
| spellingShingle | Zhuoya Ni Mengdie Wu Qifeng Lu Hongyuan Huo Chunqiang Wu Ruixia Liu Fu Wang Xiaoying Xu A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances Remote Sensing hyperspectral infrared sounder cloud detection cloud-clearing numerical weather prediction deep learning |
| title | A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances |
| title_full | A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances |
| title_fullStr | A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances |
| title_full_unstemmed | A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances |
| title_short | A Review of Research on Cloud Detection Methods for Hyperspectral Infrared Radiances |
| title_sort | review of research on cloud detection methods for hyperspectral infrared radiances |
| topic | hyperspectral infrared sounder cloud detection cloud-clearing numerical weather prediction deep learning |
| url | https://www.mdpi.com/2072-4292/16/24/4629 |
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