DOI: 10.5281/zenodo.14197227
title: Ship wakes observed by Synthetic Aperture Radar augmented by manually retraced wake components
title: SAR-based training dataset for wake component detection

description: ###the following abstract is also provided as a file using proper formatting###

Abstract: Ship wakes observed by Synthetic Aperture Radar augmented by manually retraced wake components

Satellite-based Synthetic Aperture Radar (SAR) sensors offer the opportunity to observe the maritime domain, even during nighttime and under foggy or cloudy weather conditions. Depending on the nature of oceanographic observations, gaining information of position and movement of maritime objects is an essential element. Radar signatures of man-made maritime objects typically have extents of up to a few hundred meters. However, the transit of a moving ship can affect the ocean surface up to hundreds of kilometers creating large scale artefacts in SAR images, the so-called wake signatures. The published data is focused on the observation of moving ships by exploiting those wake signatures imaged by the SAR sensors.

The appearance of ship wakes in SAR imagery has been investigated for decades. Radar signatures of ship wakes are complex structures consisting of multiple wake components. Those wake components appear with different shapes and extents in SAR acquisitions, depending on various influencing parameters describing the present situation during the observation. Those influencing parameters are categorized into three types: ship properties, environmental conditions and image acquisition parameters.

Recently, the characteristic effect of the influencing parameters on the detectability of ship wakes has been modelled and systematically analyzed for the first time on the basis of this dataset, now available to the public. The results are published in the following journal publications [1, 2, 3, 4, 5, 6, 7] and all-encompassing in the following dissertation’s monography [8]. The published dataset has also been applied to develop the first Deep-Learning-based detector for individual wake components in SAR imagery [9].

This published dataset offers the following unique features:



This dataset contains extracted image patches of SAR acquisitions from the SAR missions TerraSAR X, CosmoSkymed, Sentinel 1 and RADARSAT 2 in tiff file format. The X-band and C-band radar frequencies of the SAR sensors operated by those four missions are an ideal choice for indirect detection of ships on the ocean surface. The acquisitions were taken in the years 2013 to 2018 over North Sea, Baltic Sea and Mediterranean Sea.

Each image patch contains the position of a moving ship, i.e. a candidate wake sample, with 5.1 km x 5.1 km extent and pixel spacing of 1.5 m. Each image patch is complemented with metadata information and information on influencing parameters in ods file format:



ship properties, derived CFAR detection algorithm [10] and from data of the Automatic Identification System (AIS) [11],

environmental conditions, estimated using SAR-SeaStaR’s empirical model functions for wind and sea state parameter retrieval [12, 13, 14, 15] as well as Weather Research and Forecasting Model (WRF) [16], and

image acquisitions parameters, extracted from the SAR product’s metadata.



All candidate wake samples have been manually inspected by two different experts in the field of SAR oceanography. All look-a-likes of wake signatures have been filtered out. Position of individual wake components have been retraced, in case of wake components with curved characteristics, i.e. near-hull turbulence, turbulent wakes, Kelvin wake arms, V-narrow wake arms and ship-generated internal waves, or flagged, in case of wake components with oscillating characteristics, i.e. transverse waves and divergent waves. Retracing information is made available in csv file format and python source code for interpretation of all files is also delivered in the package.


The publication of this dataset shall enable users to,



reproduce the wake detection methods or modelling and systematical analysis of wake detectability developed and published by the authors [1, 2, 3, 4, 5, 6, 7, 8], and

develop their own methods for recognition of ship wakes in SAR imagery.


Acknowledgments



Data provided by the European Space Agency.

Includes material from COSMO-SkyMed satellite image © ASI (2018 & 2019), provided by e-GEOS, all rights reserved. Please note: the extracts of CosmoSkymed (CSK) images exceed the maximum dimensions allowed by e-GEOS for data publication by 24 pixels in width and height dimension, respectively (i.e. 1024x1024 pixels instead of 1000x1000 pixels), as restricted in ESA's TPM terms and conditions. E-GEOS has given their written consent to the publishing authors that the extracts from CSK images can be published in their current form.

RADARSAT is an official mark of the Canadian Space Agency. RADARSAT-2 Data and Products @ MDA Geospatial Services Inc. (2013 to 2019) — All Rights Reserved

Contains modified Copernicus Sentinel data 2015.

TerraSAR-X/TanDEM-Y data © DLR <2013 to 2017>


References

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