Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images
Cloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monito...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2409966 |
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| author | Long Gao Liyuan Li Jianing Yu Xiaoxuan Zhou Lu Zou Nan Fang Xiaofeng Su Fansheng Chen |
| author_facet | Long Gao Liyuan Li Jianing Yu Xiaoxuan Zhou Lu Zou Nan Fang Xiaofeng Su Fansheng Chen |
| author_sort | Long Gao |
| collection | DOAJ |
| description | Cloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monitoring. Convolutional neural networks have shown outstanding performance in previous cloud detection algorithms due to their robust ability to extract local information. However, their inherent inductive bias limits their capacity to learn long-range semantic information. To address these challenges, we proposed SwinCloud, a U-shaped semantic segmentation network based on an enhanced Swin Transformer for cloud detection in the thermal infrared spectral range. Specifically, we augment the Swin Transformer's window attention module with a CNN-based parallel pathway to effectively model global-local information. We employ a feature fusion module before the final upsampling module in the decoder to better integrate low-level spatial information and high-level semantic information. On the Landsat-8 cloud detection dataset, our model outperforms state-of-the-art methods. When transferred to the SDGSAT-TIS cloud detection dataset, the mIOU of experiment results reaches 69.9%, demonstrating the strong transferability of SwinCloud across different sensors. We also applied SwinCloud to cloud detection in the visible bands of Landsat-8. The results demonstrated SwinCloud's generalization capability across different bands. |
| format | Article |
| id | doaj-art-db16f457d4c34104a3f1b19a1ac627a4 |
| institution | OA Journals |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-db16f457d4c34104a3f1b19a1ac627a42025-08-20T01:47:37ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2409966Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing imagesLong Gao0Liyuan Li1Jianing Yu2Xiaoxuan Zhou3Lu Zou4Nan Fang5Xiaofeng Su6Fansheng Chen7Key Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of ChinaShanghai Frontier Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics, Fudan University, Shanghai, People’s Republic of ChinaUniversity of Chinese Academy of Sciences, Beijing, People’s Republic of ChinaKey Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of ChinaChina Xi’an Satellite Control Center, the State Key Laboratory of Astronautic Dynamics, Xi’an, People’s Republic of ChinaKey Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of ChinaKey Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of ChinaKey Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, People’s Republic of ChinaCloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monitoring. Convolutional neural networks have shown outstanding performance in previous cloud detection algorithms due to their robust ability to extract local information. However, their inherent inductive bias limits their capacity to learn long-range semantic information. To address these challenges, we proposed SwinCloud, a U-shaped semantic segmentation network based on an enhanced Swin Transformer for cloud detection in the thermal infrared spectral range. Specifically, we augment the Swin Transformer's window attention module with a CNN-based parallel pathway to effectively model global-local information. We employ a feature fusion module before the final upsampling module in the decoder to better integrate low-level spatial information and high-level semantic information. On the Landsat-8 cloud detection dataset, our model outperforms state-of-the-art methods. When transferred to the SDGSAT-TIS cloud detection dataset, the mIOU of experiment results reaches 69.9%, demonstrating the strong transferability of SwinCloud across different sensors. We also applied SwinCloud to cloud detection in the visible bands of Landsat-8. The results demonstrated SwinCloud's generalization capability across different bands.https://www.tandfonline.com/doi/10.1080/17538947.2024.2409966Cloud detectionSwin TransformerSDGSAT-1Landsat-8thermal infrared |
| spellingShingle | Long Gao Liyuan Li Jianing Yu Xiaoxuan Zhou Lu Zou Nan Fang Xiaofeng Su Fansheng Chen Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images International Journal of Digital Earth Cloud detection Swin Transformer SDGSAT-1 Landsat-8 thermal infrared |
| title | Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images |
| title_full | Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images |
| title_fullStr | Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images |
| title_full_unstemmed | Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images |
| title_short | Swincloud: a hybrid network for cloud detection in thermal infrared remote sensing images |
| title_sort | swincloud a hybrid network for cloud detection in thermal infrared remote sensing images |
| topic | Cloud detection Swin Transformer SDGSAT-1 Landsat-8 thermal infrared |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2409966 |
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