END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SATELLITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SATELLITE ALTIMETRY AND SEA SURFACE CURRENTS
This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we sho...
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| Главные авторы: | , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Copernicus Publications
2021-06-01
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| Серии: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online-ссылка: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.pdf |
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