FN Archimer Export Format PT J TI A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode BT AF Wang, Chen Mouche, Alexis Tandeo, Pierre Stopa, Justin Longépé, Nicolas Erhard, Guillaume Foster, Ralph C. Vandemark, Douglas Chapron, Bertrand AS 1:1,2;2:1;3:2;4:3;5:4;6:4;7:5;8:6;9:1; FF 1:PDG-ODE-LOPS-SIAM;2:PDG-ODE-LOPS-SIAM;3:;4:PDG-ODE-LOPS-SIAM;5:;6:;7:;8:;9:PDG-ODE-LOPS-SIAM; C1 Laboratoire d'Océanographie Physique et Spatiale (LOPS) IFREMER, University in Brest, CNRS, IRD Brest ,France Lab‐STICC IMT Atlantique, UBL Brest ,France Department of Ocean Resources and Engineering University of Hawaii at Manoa Honolulu Hawaii ,USA Space and Ground Segment Collecte Localisation Satellites (CLS) Plouzané, France Applied Physics Laboratory University of Washington Seattle Washington, USA Ocean Processes Analysis Laboratory University of New Hampshire Durham New Hampshire, USA C2 IFREMER, FRANCE IMT ATLANTIQUE, FRANCE UNIV HAWAII MANOA, USA CLS, FRANCE UNIV WASHINGTON, USA UNIV NEW HAMPSHIRE, USA SI BREST SE PDG-ODE-LOPS-SIAM UM LOPS IN WOS Ifremer UMR DOAJ copubli-france copubli-int-hors-europe IF 2.714 TC 34 UR https://archimer.ifremer.fr/doc/00512/62406/66659.pdf LA English DT Article DE ;manual labelling;ocean surface phenomena;Sentinel-1 wave mode;Synthetic aperture radar AB 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. PY 2019 PD NOV SO Geoscience Data Journal SN 2049-6060 PU Wiley VL 6 IS 2 UT 000479540200001 BP 105 EP 115 DI 10.1002/gdj3.73 ID 62406 ER EF