A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode

Type Article
Date 2019-11
Language English
Author(s) Wang Chen1, 2, Mouche AlexisORCID1, Tandeo Pierre2, Stopa Justin3, Longépé Nicolas4, Erhard Guillaume4, Foster Ralph C.5, Vandemark Douglas6, Chapron Bertrand1
Affiliation(s) 1 : Laboratoire d'Océanographie Physique et Spatiale (LOPS) IFREMER, University in Brest, CNRS, IRD Brest ,France
2 : Lab‐STICC IMT Atlantique, UBL Brest ,France
3 : Department of Ocean Resources and Engineering University of Hawaii at Manoa Honolulu Hawaii ,USA
4 : Space and Ground Segment Collecte Localisation Satellites (CLS) Plouzané, France
5 : Applied Physics Laboratory University of Washington Seattle Washington, USA
6 : Ocean Processes Analysis Laboratory University of New Hampshire Durham New Hampshire, USA
Source Geoscience Data Journal (2049-6060) (Wiley), 2019-11 , Vol. 6 , N. 2 , P. 105-115
DOI 10.1002/gdj3.73
WOS© Times Cited 1
Keyword(s) manual labelling, ocean surface phenomena, Sentinel-1 wave mode, Synthetic aperture radar

The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

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Wang Chen, Mouche Alexis, Tandeo Pierre, Stopa Justin, Longépé Nicolas, Erhard Guillaume, Foster Ralph C., Vandemark Douglas, Chapron Bertrand (2019). A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode. Geoscience Data Journal, 6(2), 105-115. Publisher's official version : https://doi.org/10.1002/gdj3.73 , Open Access version : https://archimer.ifremer.fr/doc/00512/62406/