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Comparison of ship wake detectability on C-band and X-band SAR

Tings, Björn and Velotto, Domenico (2018) Comparison of ship wake detectability on C-band and X-band SAR. International Journal of Remote Sensing, 39 (13), pp. 4451-4468. Taylor & Francis. doi: 10.1080/01431161.2018.1425568. ISSN 0143-1161.

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Official URL: https://doi.org/10.1080/01431161.2018.1425568


This article describes how a detectability model can be trained in the form of a binary classifier from a data set of synthetic aperture radar (SAR) images of ship wakes, augmented by automatic identification system data. While detectability models for ship signatures exist, ship wake detectability models are only available for simulated data. In order to improve existing ship wake detection algorithms on SAR imagery, there is a need for building a data-driven detectability model which may provide useful a-priori information. A binary L2-regularized logistic regression classifier is trained for each investigated data subset. The dependency on the SAR working frequency is evaluated by analysing a large number of X- and C-band images. In the X-band, the probability of detecting a wake shows dependencies on vessel size and velocity as well as prevailing wind speed. In the C-band, these dependencies are maintained, but with a general reduction in the correlation. This fact led us to the conclusion that, for our data set, ship wakes are more easily imaged in the X-band rather than in the C-band. This is an important outcome, which is supported by a qualitative and quantitative analysis of a large data set of TerraSAR-X and two independent C-band sensors, specifically RADARSAT-2 and Sentinel-1.

Item URL in elib:https://elib.dlr.de/108121/
Document Type:Article
Title:Comparison of ship wake detectability on C-band and X-band SAR
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Tings, Björnbjoern.tings (at) dlr.dehttps://orcid.org/0000-0002-1945-6433
Velotto, Domenicodomenico.velotto (at) dlr.dehttps://orcid.org/0000-0002-8592-0652
Date:15 January 2018
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1080/01431161.2018.1425568
Page Range:pp. 4451-4468
Publisher:Taylor & Francis
Keywords:ship wake detection, machine learning, Synthetic Aperture Radar, SAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - SAR methods
Location: Bremen , Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Kaps, Ruth
Deposited On:24 Jan 2018 12:22
Last Modified:31 Jul 2019 20:05

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