CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution
Meteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible to infrared. However, the spatial resolution of the...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2513 |
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| author | Weiliang Liang Yuan Liu |
| author_facet | Weiliang Liang Yuan Liu |
| author_sort | Weiliang Liang |
| collection | DOAJ |
| description | Meteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible to infrared. However, the spatial resolution of these bands varies, with images at longer wavelengths typically exhibiting lower spatial resolutions, which limits the accuracy and reliability of subsequent applications. To alleviate this issue, we propose a channel–spatial attention-based network, named CSAN, designed to super-resolve all low-resolution (LR) bands to the available maximal high-resolution (HR) scale. The CSAN consists of an information fusion unit, a feature extraction module, and an image restoration unit. The information fusion unit adaptively fuses LR and HR images, effectively capturing inter-band spectral relationships and spatial details to enhance the input representation. The feature extraction module integrates channel and spatial attention into the residual network, enabling the extraction of informative spectral and spatial features from the fused inputs. Using these deep features, the image restoration unit reconstructs the missing spatial details in LR images. Extensive experiments demonstrate that the proposed network outperforms other state-of-the-art approaches quantitatively and visually. |
| format | Article |
| id | doaj-art-ffd7b275bf414c3bbfb0a5ce5fbd5711 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ffd7b275bf414c3bbfb0a5ce5fbd57112025-08-20T03:32:33ZengMDPI AGRemote Sensing2072-42922025-07-011714251310.3390/rs17142513CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-ResolutionWeiliang Liang0Yuan Liu1School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, ChinaMeteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible to infrared. However, the spatial resolution of these bands varies, with images at longer wavelengths typically exhibiting lower spatial resolutions, which limits the accuracy and reliability of subsequent applications. To alleviate this issue, we propose a channel–spatial attention-based network, named CSAN, designed to super-resolve all low-resolution (LR) bands to the available maximal high-resolution (HR) scale. The CSAN consists of an information fusion unit, a feature extraction module, and an image restoration unit. The information fusion unit adaptively fuses LR and HR images, effectively capturing inter-band spectral relationships and spatial details to enhance the input representation. The feature extraction module integrates channel and spatial attention into the residual network, enabling the extraction of informative spectral and spatial features from the fused inputs. Using these deep features, the image restoration unit reconstructs the missing spatial details in LR images. Extensive experiments demonstrate that the proposed network outperforms other state-of-the-art approaches quantitatively and visually.https://www.mdpi.com/2072-4292/17/14/2513super-resolutiondeep learningmeteorological satellite imagechannel–spatial attention |
| spellingShingle | Weiliang Liang Yuan Liu CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution Remote Sensing super-resolution deep learning meteorological satellite image channel–spatial attention |
| title | CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution |
| title_full | CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution |
| title_fullStr | CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution |
| title_full_unstemmed | CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution |
| title_short | CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution |
| title_sort | csan a channel spatial attention based network for meteorological satellite image super resolution |
| topic | super-resolution deep learning meteorological satellite image channel–spatial attention |
| url | https://www.mdpi.com/2072-4292/17/14/2513 |
| work_keys_str_mv | AT weiliangliang csanachannelspatialattentionbasednetworkformeteorologicalsatelliteimagesuperresolution AT yuanliu csanachannelspatialattentionbasednetworkformeteorologicalsatelliteimagesuperresolution |