Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912408273944576 |
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| author | Retnanto, Aditya Le, Son Mueller, Sebastian Leitner, Armin Riffler, Michael Schindler, Konrad Iddawela, Yohan |
| author_facet | Retnanto, Aditya Le, Son Mueller, Sebastian Leitner, Armin Riffler, Michael Schindler, Konrad Iddawela, Yohan |
| contents | Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond naïve upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages of single-image and multi-image techniques. It combines temporal oversampling from repeated Sentinel-2 acquisitions with a learned prior from high-resolution Pléiades Neo data. In doing so, SEN4X upgrades Sentinel-2 imagery to 2.5 m ground sampling distance. We test the super-resolved images on urban land-cover classification in Hanoi, Vietnam. We find that they lead to a significant performance improvement over state-of-the-art super-resolution baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images Retnanto, Aditya Le, Son Mueller, Sebastian Leitner, Armin Riffler, Michael Schindler, Konrad Iddawela, Yohan Image and Video Processing Computer Vision and Pattern Recognition Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond naïve upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages of single-image and multi-image techniques. It combines temporal oversampling from repeated Sentinel-2 acquisitions with a learned prior from high-resolution Pléiades Neo data. In doing so, SEN4X upgrades Sentinel-2 imagery to 2.5 m ground sampling distance. We test the super-resolved images on urban land-cover classification in Hanoi, Vietnam. We find that they lead to a significant performance improvement over state-of-the-art super-resolution baselines. |
| title | Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.24799 |