| Title: |
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks |
| Authors: |
Colin, Aurélien; Tandeo, Pierre; Peureux, Charles; Husson, Romain; Fablet, Ronan |
| Contributors: |
Equipe Observations Signal & Environnement (Lab-STICC_OSE); Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB); Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB); Institut Mines-Télécom Paris (IMT); Département Mathematical and Electrical Engineering (IMT Atlantique - MEE); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Océan Dynamique Observations Analyse (ODYSSEY); Université de Rennes (UR)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO EPE)-Centre Inria de l'Université de Rennes; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IMT Atlantique (IMT Atlantique); Collecte Localisation Satellites (CLS) |
| Source: |
ISSN: 1939-1404 ; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ; https://imt-atlantique.hal.science/hal-04149533 ; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, pp.1 - 13. ⟨10.1109/jstars.2023.3291236⟩. |
| Publisher Information: |
CCSD; IEEE |
| Publication Year: |
2023 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
Synthetic Aperture Radar; Deep Learning; Oceanography; Wind; [SDE]Environmental Sciences |
| Description: |
International audience ; Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has difficulties accounting for non-wind processes such as rain events. Convolutional neural network, on the other hand, have the capacity to use contextual information and have demonstrated their ability to delimit rainfall areas. By carefully building a large dataset of SAR observations from the Copernicus Sentinel-1 mission, collocated with both GMF and atmospheric model wind speeds as well as rainfall estimates, we were able to train a wind speed estimator with reduced errors under rain. Collocations with in-situ wind speed measurements from buoys show a root mean square error that is reduced by 27% (resp. 45%) under rainfall estimated at more than 1 mm/h (resp. 3 mm/h). These results demonstrate the capacity of deep learning models to correct rain-related errors in SAR products. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1109/jstars.2023.3291236 |
| Availability: |
https://imt-atlantique.hal.science/hal-04149533; https://imt-atlantique.hal.science/hal-04149533v1/document; https://imt-atlantique.hal.science/hal-04149533v1/file/10168970.pdf; https://doi.org/10.1109/jstars.2023.3291236 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.346C36CC |
| Database: |
BASE |