Sentinel-1 wave mode
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MulTags-SARwv is a meticulously curated dataset comprising 2100 synthetic aperture radar (SAR) images acquired by the Sentinel-1 (S-1) satellite s wave mode (WV) at a 36° incidence angle. These images were randomly selected between January and June from 2016-2019 and span an 8° by 8° box centered on the Northwest Tropical Atlantic Station (NTAS) buoy (51°W, 15°N). Each image covers a 20 km by 20 km area with a 5 m pixel resolution. The dataset is the result of visual interpretation and tagging by five experts using 12 defined tags. These tags describe various marine atmospheric boundary layer (MABL) processes near Barbados, including the transition between roll vortices and convective cells, as well as localized phenomena such as fronts, rain events, cold pools, and low winds. Unlike the previous TenGeoP-SARwv dataset, MulTags-SARwv utilizes a multiple tagging strategy to provide a comprehensive description of the MABL processes visible on these WV SAR images. The dataset is organized into a folder (pngs) that includes all 2100 SAR images in PNG format, a text file (tagsInfo.txt) documenting all the assigned tags for each image by the five experts, and a readme.txt file that provides an overall description of the dataset. MulTags-SARwv has significant potential for investigating MABL processes and complements SAR classification materials for the development of machine learning methods.
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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