Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes
In mountainous landscapes, integrating topographical information is crucial for effective analysis and understanding. Remote sensing becomes indispensable in studying mountains, enabling the monitoring of critical aspects such as grassland degradation, snow depth, glacier dynamics. The intricate nat...
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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/11009194/ |
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| author | Meryeme Boumahdi Consuelo Gonzalo-Martin Mario Lillo-Saavedra Marcelo Somos-Valenzuela Angel Garcia-Pedrero |
| author_facet | Meryeme Boumahdi Consuelo Gonzalo-Martin Mario Lillo-Saavedra Marcelo Somos-Valenzuela Angel Garcia-Pedrero |
| author_sort | Meryeme Boumahdi |
| collection | DOAJ |
| description | In mountainous landscapes, integrating topographical information is crucial for effective analysis and understanding. Remote sensing becomes indispensable in studying mountains, enabling the monitoring of critical aspects such as grassland degradation, snow depth, glacier dynamics. The intricate nature of mountain ecosystems requires a thorough understanding of their dynamics, given their vital role in providing habitats for unique species, influencing hydrology patterns, and indicating the impact of climate change. However, the persistent challenge of cloud cover obstructs surface observations, limiting the effectiveness of optical sensors. To overcome this obstacle, various cloud removal techniques have been developed, although they tend to struggle in such landscapes. In this study, we introduce CRT-UNet, a UNet-based cloud removal model that incorporates topographical information from digital elevation model (DEM) data as input to enhance performance in mountainous regions. By integrating synthetic aperture radar Sentinel-1 (S1) data, DEM information, and topographic insights into Sentinel-2 (S2) data, our model aims to enhance cloud removal capabilities, particularly in challenging terrains characterized by thick cloud coverage and significant elevation variations. This integration of topographical information enriches the cloud removal process, enabling more accurate restoration of obscured terrain features. The results demonstrate the superior performance of CRT-UNet in cloud removal across varying cloud cover levels and complex terrain features, such as rugged peaks and deep valleys. The proposed model outperforms state-of-the-art cloud removal models quantitatively and qualitatively. This underscores the importance of incorporating topographical information in remote sensing applications, particularly in mountainous regions, to improve data accuracy. |
| format | Article |
| id | doaj-art-4479b5fe1b6645d984a23e6f14f6eefa |
| institution | OA Journals |
| 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-4479b5fe1b6645d984a23e6f14f6eefa2025-08-20T02:32:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118134891350310.1109/JSTARS.2025.357237911009194Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous LandscapesMeryeme Boumahdi0https://orcid.org/0000-0003-0600-8137Consuelo Gonzalo-Martin1https://orcid.org/0000-0002-0804-9293Mario Lillo-Saavedra2https://orcid.org/0000-0001-5634-9162Marcelo Somos-Valenzuela3https://orcid.org/0000-0001-7863-4407Angel Garcia-Pedrero4https://orcid.org/0000-0002-6848-481XCenter for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, SpainCenter for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, SpainFacultad de Ingeniería Agrícola, Universidad de Concepción, Chillán, ChileDepartment of Forest Sciences, Faculty of Agriculture and Environmental Sciences, University of La Frontera, Temuco, ChileCenter for Biomedical Technology, Universidad Politécnica de Madrid, Pozuelo de Alarcón, SpainIn mountainous landscapes, integrating topographical information is crucial for effective analysis and understanding. Remote sensing becomes indispensable in studying mountains, enabling the monitoring of critical aspects such as grassland degradation, snow depth, glacier dynamics. The intricate nature of mountain ecosystems requires a thorough understanding of their dynamics, given their vital role in providing habitats for unique species, influencing hydrology patterns, and indicating the impact of climate change. However, the persistent challenge of cloud cover obstructs surface observations, limiting the effectiveness of optical sensors. To overcome this obstacle, various cloud removal techniques have been developed, although they tend to struggle in such landscapes. In this study, we introduce CRT-UNet, a UNet-based cloud removal model that incorporates topographical information from digital elevation model (DEM) data as input to enhance performance in mountainous regions. By integrating synthetic aperture radar Sentinel-1 (S1) data, DEM information, and topographic insights into Sentinel-2 (S2) data, our model aims to enhance cloud removal capabilities, particularly in challenging terrains characterized by thick cloud coverage and significant elevation variations. This integration of topographical information enriches the cloud removal process, enabling more accurate restoration of obscured terrain features. The results demonstrate the superior performance of CRT-UNet in cloud removal across varying cloud cover levels and complex terrain features, such as rugged peaks and deep valleys. The proposed model outperforms state-of-the-art cloud removal models quantitatively and qualitatively. This underscores the importance of incorporating topographical information in remote sensing applications, particularly in mountainous regions, to improve data accuracy.https://ieeexplore.ieee.org/document/11009194/Cloud removalmountainous areaSentinel-1 (S1)Sentinel-2 (S2)topographic dataUNet |
| spellingShingle | Meryeme Boumahdi Consuelo Gonzalo-Martin Mario Lillo-Saavedra Marcelo Somos-Valenzuela Angel Garcia-Pedrero Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloud removal mountainous area Sentinel-1 (S1) Sentinel-2 (S2) topographic data UNet |
| title | Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes |
| title_full | Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes |
| title_fullStr | Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes |
| title_full_unstemmed | Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes |
| title_short | Multisource Topographic-Enhanced Cloud Removal for Remote Sensing in Mountainous Landscapes |
| title_sort | multisource topographic enhanced cloud removal for remote sensing in mountainous landscapes |
| topic | Cloud removal mountainous area Sentinel-1 (S1) Sentinel-2 (S2) topographic data UNet |
| url | https://ieeexplore.ieee.org/document/11009194/ |
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