seanoe:56796 data@seanoe.org 2025-06-05 ISO 19115:2003/19139 1.0 Labeled SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode (TenGeoP-SARwv) 2018 2025-06-05 Wang Chen, Mouche Alexis, Tandeo Pierre, Stopa Justin, Longépé Nicolas, Erhard Guillaume, Foster Ralph, Vandemark Douglas, Chapron Bertrand (2018). Labeled SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode (TenGeoP-SARwv). SEANOE. https://doi.org/10.17882/56796 In addition to properly cite this dataset, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : 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. https://doi.org/10.1002/gdj3.73 The TenGeoP-SARwv dataset is established based on the acquisitions of Sentinel-1A wave mode (WV) in VV polarization. This dataset consists of more than 37,000 SAR vignettes divided into ten defined geophysical categories, 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 phenomena with its prescribed signature and texture is selected for labeling. 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 is 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 WANG Chen Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Brest, France Institut Mines-Télécom Atlantique, UMR 6285 LabSTICC, Université Bretagne Loire, Technopôle Brest-Iroise CS 83818, 29238 Brest Cedex 3, France MOUCHE Alexis Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Brest, France Tandeo Pierre Institut Mines-Télécom Atlantique, UMR 6285 LabSTICC, Université Bretagne Loire, Technopôle Brest-Iroise CS 83818, 29238 Brest Cedex 3, France Stopa Justin Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Brest, France Longépé Nicolas Space and Ground Segment, Collecte Localisation Satellites (CLS), Plouzané, France Erhard Guillaume Space and Ground Segment, Collecte Localisation Satellites (CLS), Plouzané, France Foster Ralph Applied Physics Laboratory, University of Washington, 1013 NE 40th Street, Seattle, Washington, USA Vandemark Douglas Ocean Processes Analysis Laboratory, University of New Hampshire, New Hampshire, USA CHAPRON Bertrand Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Brest, France SEANOE https://www.seanoe.org/data/00456/56796/illustration.gif Synthetic aperture radar (SAR) Sentinel-1 wave mode SAR images Geophysical phenomena Classification Manually labeling Physical oceanography Cross-discipline Environment Currents Turbidity Waves Wind ODATIS aggregation parameters and Essential Variable names 2024-09-16 geonetwork.thesaurus.external.theme.NVS.OD1 Physical oceanography Cross-discipline Environment SeaDataNet Parameter Disciplines 2021-04-07 geonetwork.thesaurus.external.parameter.NVS.P08 /Observational data/in-situ Type de jeux de donnée ODATIS 2024-10-15 geonetwork.thesaurus.local.theme.type_jeux_donnee CC-BY-NC-SA (Creative Commons - Attribution, No commercial usage, Sharing under the same conditions) A multi-tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions 2025 Wiley Wang Chen Stopa Justin Vandemark Doug Foster Ralph Ayet Alex Mouche Alexis Chapron Bertrand Sadowski Peter 10.1002/gdj3.282 Use of Vision Transformer to Classify Sea Surface Phenomena in SAR Imagery 2025 Institute of Electrical and Electronics Engineers (IEEE) Xia Junfei Romeiser Roland Zhang Wei Özgökmen Tamay 10.1109/jstars.2025.3558673 A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification 2024 MDPI AG Bai Chengzu Zhang Shuo Wang Xinning Wen Jiaqiang Li Chong 10.3390/app14041489 Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges 2024 MDPI AG Huang Wenjun Yu Anzhu Xu Qing Sun Qun Guo Wenyue Ji Song Wen Bowei Qiu Chunping 10.3390/rs16050842 Backscattering Statistics of Labeled Sentinel-1 Wave Mode Imagettes for Ten Geophysical Phenomena 2022 MDPI AG Dai Ziyue Li Huimin Wang Chen He Yijun 10.3390/jmse10111594 Sea Surface Wind Speed Retrieval From Textures in Synthetic Aperture Radar Imagery 2021 Institute of Electrical and Electronics Engineers (IEEE) Zhou Lizhang Zheng Gang Yang Jingsong Li Xiaofeng Zhang Bin Wang He Chen Peng Wang Yan 10.1109/TGRS.2021.3062401 An assessment of marine atmospheric boundary layer roll detection using Sentinel-1 SAR data 2020 Elsevier BV WANG CHEN VANDEMARK DOUGLAS MOUCHE ALEXIS CHAPRON BERTRAND LI HUIMIN FOSTER RALPH C. 10.1016/j.rse.2020.112031 A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode 2019 Wiley WANG CHEN MOUCHE ALEXIS TANDEO PIERRE STOPA JUSTIN LONGÉPÉ NICOLAS ERHARD GUILLAUME FOSTER RALPH C. VANDEMARK DOUGLAS CHAPRON BERTRAND 10.1002/gdj3.73 Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies 2019 Elsevier BV Wang Chen Tandeo Pierre Mouche Alexis Stopa Justin Gressani Victor Longepe Nicolas Vandemark Douglas Foster Ralph C. Chapron Bertrand 10.1016/j.rse.2019.111457 A multi-tagged Sentinel-1 wave mode SAR image dataset near Barbados (MulTags-SARwv) 2023 SEANOE Wang Chen Stopa Justin Vandemark Doug Foster Ralph Ayet Alex Mouche Alexis Chapron Bertrand 10.17882/93947 oceans -180.0 180.0 -70.0 65.0 TEXTE IMAGE https://www.seanoe.org/data/00456/56796/data/58682.txt WWW:DOWNLOAD-1.0-link--download Information of ten defined geophysical categories - 164 bytes https://www.seanoe.org/data/00456/56796/data/58683.txt WWW:DOWNLOAD-1.0-link--download Information of the labeled SAR images, including file name, labeling, swath, capture time, and centre latitude and longitude - 3 MB https://www.seanoe.org/data/00456/56796/data/58684.tar.gz WWW:DOWNLOAD-1.0-link--download Processed data High quality images for machine learning approach - 15 GB https://www.seanoe.org/data/00456/56796/data/58685.tar.gz WWW:DOWNLOAD-1.0-link--download Processed data Quick-looks for human visual inspection - 16 GB https://doi.org/10.17882/56796 WWW:LINK-1.0-http--metadata-URL DOI of the product https://www.seanoe.org/data/00456/56796/ rel-canonical Seanoe The ESA Sentinel-1 mission is a constellation of two polar-orbiting, sun-synchronous satellites (S-1 A and S-1 B) launched in April of 2014 and 2016, respectively. These two satellites both have a 12-day repeat cycle at the equator, and are phased at 180 deg to provide an effective 6-day repeat cycle. For each satellite, the expected lifetime is 7 years. Both carry a C-band SAR instrument with a center frequency of 5.405 GHz (5.5 cm wavelength). There are four exclusive imaging modes (Interferometric Wide swath, Extra Wide swath mode, Strip Map and Wave Mode) for the S-1 SAR sensors. WV is the default operational mode over open ocean unless wide-swath SAR images are requested for particular applications.