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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07392 |
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| _version_ | 1866912817970413568 |
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| author | Tuel, Alexandre Kerdreux, Thomas Febvre, Quentin Mouche, Alexis Grouazel, Antoine Miadana, Jean-Renaud Audras, Antoine Wang, Chen Chapron, Bertrand |
| author_facet | Tuel, Alexandre Kerdreux, Thomas Febvre, Quentin Mouche, Alexis Grouazel, Antoine Miadana, Jean-Renaud Audras, Antoine Wang, Chen Chapron, Bertrand |
| contents | We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies, which enhances performance while reducing training cost. OceanSAR-2 demonstrates strong transfer performance across downstream tasks, including geophysical pattern classification, ocean surface wind vector and significant wave height estimation, and iceberg detection. We release standardized benchmark datasets, providing a foundation for systematic evaluation and advancement of SAR models for ocean applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07392 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation Tuel, Alexandre Kerdreux, Thomas Febvre, Quentin Mouche, Alexis Grouazel, Antoine Miadana, Jean-Renaud Audras, Antoine Wang, Chen Chapron, Bertrand Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies, which enhances performance while reducing training cost. OceanSAR-2 demonstrates strong transfer performance across downstream tasks, including geophysical pattern classification, ocean surface wind vector and significant wave height estimation, and iceberg detection. We release standardized benchmark datasets, providing a foundation for systematic evaluation and advancement of SAR models for ocean applications. |
| title | OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.07392 |