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

Geoscience Data Journal

Ralph C. Foster¹  Justin E. Stopa²  Bertrand Chapron³  Alexis Mouche³  Chen Wang³  Pierre Tandeo⁴  Douglas Vandemark⁵  Nicolas Longépé⁶  Guillaume Erhard⁶ 

[1] Applied Physics Laboratory University of Washington Seattle Washington USA;Department of Ocean Resources and Engineering University of Hawaii at Manoa Honolulu Hawaii USA;Laboratoire d'Océanographie Physique et Spatiale (LOPS) IFREMER, University in Brest, CNRS, IRD Brest France;Lab‐STICC IMT Atlantique, UBL Brest France;Ocean Processes Analysis Laboratory University of New Hampshire Durham New Hampshire USA;Space and Ground Segment Collecte Localisation Satellites (CLS) Plouzané France;

关键词: manual labelling;    ocean surface phenomena;    Sentinel-1 wave mode;    Synthetic aperture radar;   

DOI  :  10.1002/gdj3.73

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown