| Title: |
Rain Regime Segmentation of Sentinel-1 Observation Learning From NEXRAD Collocations With Convolution Neural Networks |
| Authors: |
Colin, Aurélien; Tandeo, Pierre; Peureux, Charles; Husson, Romain; Longépé, Nicolas; Fablet, Ronan |
| Contributors: |
Collecte Localisation Satellites (CLS); Département Mathematical and Electrical Engineering (IMT Atlantique - MEE); IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); 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); 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); Agence Spatiale Européenne = European Space Agency (ESA) |
| Source: |
ISSN: 0196-2892 ; IEEE Transactions on Geoscience and Remote Sensing ; https://imt-atlantique.hal.science/hal-04502265 ; IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, pp.1-14. ⟨10.1109/TGRS.2024.3353311⟩. |
| Publisher Information: |
CCSD; Institute of Electrical and Electronics Engineers |
| Publication Year: |
2024 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
Aperture Radar; Deep Learning; Oceanography; Rainfall; [SDU.OCEAN]Sciences of the Universe [physics]/Ocean; Atmosphere |
| Description: |
International audience ; Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA’s Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainfall events. However, their observation range is limited to a few hundred kilometers, prompting the exploration of other remote sensing methods, particularly over the open ocean, that represents large areas not covered by land-based radars.Here we propose a deep learning approach to deliver a three-classsegmentation of SAR observations in terms of rainfall regimes. SAR satellites deliver very high resolution observations with a global coverage. This seems particularly appealing to inform fine-scale rain-related patterns, such as those associated with convective cells with characteristic scales of a few kilometers. We demonstrate that a convolutional neural network trained on a collocated Sentinel-1/NEXRAD dataset clearly outperforms stateof- the-art filtering schemes such as the Koch’s filters. Our results indicate high performance in segmenting precipitation regimes, delineated by thresholds at 24.7, 31.5, and 38.8 dBZ. Compared to current methods that rely on Koch’s filters to draw binary rainfall maps, these multi-threshold learning-based models canprovide rainfall estimation. They may be of interest in improving high-resolution SAR-derived wind fields, which are degraded by rainfall, and provide an additional tool for the study of rain cells. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/arxiv/2207.07333; ARXIV: 2207.07333 |
| DOI: |
10.1109/TGRS.2024.3353311 |
| Availability: |
https://imt-atlantique.hal.science/hal-04502265; https://imt-atlantique.hal.science/hal-04502265v1/document; https://imt-atlantique.hal.science/hal-04502265v1/file/2207.07333.pdf; https://doi.org/10.1109/TGRS.2024.3353311 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.AFDDBC76 |
| Database: |
BASE |