| Title: |
Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning |
| Authors: |
Colin, Aurélien; Fablet, Ronan; Tandeo, Pierre; Husson, Romain; Peureux, Charles; Longépé, Nicolas; Mouche, Alexis |
| Contributors: |
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); Collecte Localisation Satellites (CLS); 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); Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER); Laboratoire d'Océanographie Physique et Spatiale (LOPS); Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO EPE)-Centre National de la Recherche Scientifique (CNRS) |
| Source: |
ISSN: 2072-4292 ; Remote Sensing ; https://imt-atlantique.hal.science/hal-03616058 ; Remote Sensing, 2022, 14 (4), pp.851. ⟨10.3390/rs14040851⟩. |
| Publisher Information: |
CCSD; MDPI |
| Publication Year: |
2022 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
Sentinel-1; weakly-supervised learning; supervised learning; deep learning; metocean; segmentation; SAR; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [SDE.IE]Environmental Sciences/Environmental Engineering |
| Description: |
International audience ; Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are distinguishable on these acquisitions. This paper studies the semantic segmentation of ten metoceanic processes either in the context of a large quantity of image-level groundtruths (i.e., weakly-supervised framework) or of scarce pixel-level groundtruths (i.e., fully-supervised framework). Our main result is that a fully-supervised model outperforms any tested weakly-supervised algorithm. Adding more segmentation examples in the training set would further increase the precision of the predictions. Trained on 20 × 20 km imagettes acquired from the WV acquisition mode of the Sentinel-1 mission, the model is shown to generalize, under some assumptions, to wide-swath SAR data, which further extents its application domain to coastal areas |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.3390/rs14040851 |
| Availability: |
https://imt-atlantique.hal.science/hal-03616058; https://imt-atlantique.hal.science/hal-03616058v1/document; https://imt-atlantique.hal.science/hal-03616058v1/file/remotesensing-14-00851.pdf; https://doi.org/10.3390/rs14040851 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.1878F023 |
| Database: |
BASE |