A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery

In the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tack...

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Main Authors: Elena Grosso, Raffaella Guida
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/24/6223
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author Elena Grosso
Raffaella Guida
author_facet Elena Grosso
Raffaella Guida
author_sort Elena Grosso
collection DOAJ
description In the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tackles the problem of ship detection in situations where the ship’s RCS is not measurable but its wake is. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified through its wake, its position in the image, its heading and its speed are determined. The algorithm was developed, tested and validated on SAR imagery from the European Space Agency mission Sentinel-1; the results are here presented and discussed. The algorithm was validated on a total of 43 images and resulted in a ship detection rate of 93% with wakes’ components recognized in more than 80% of the cases analysed.
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spelling doaj.art-13c1c305dacf476da8a2049e12a8c70f2023-11-24T17:46:07ZengMDPI AGRemote Sensing2072-42922022-12-011424622310.3390/rs14246223A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 ImageryElena Grosso0Raffaella Guida1Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UKSurrey Space Centre, University of Surrey, Guildford GU2 7XH, UKIn the field of maritime surveillance with satellite imagery, ship detection with Synthetic Aperture Radar (SAR) is largely successful with the exception of those cases where the target Radar Cross Section (RCS) results very low and hard to detect. The paper presents an automatic algorithm that tackles the problem of ship detection in situations where the ship’s RCS is not measurable but its wake is. At this purpose, the algorithm proposed uses a combination of image processing techniques in order to identify ships exclusively by detecting the wakes they leave behind. Once a ship is identified through its wake, its position in the image, its heading and its speed are determined. The algorithm was developed, tested and validated on SAR imagery from the European Space Agency mission Sentinel-1; the results are here presented and discussed. The algorithm was validated on a total of 43 images and resulted in a ship detection rate of 93% with wakes’ components recognized in more than 80% of the cases analysed.https://www.mdpi.com/2072-4292/14/24/6223remote sensingSARship detectionwake detectionSentinel-1
spellingShingle Elena Grosso
Raffaella Guida
A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
Remote Sensing
remote sensing
SAR
ship detection
wake detection
Sentinel-1
title A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
title_full A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
title_fullStr A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
title_full_unstemmed A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
title_short A New Automated Ship Wake Detector for Small and Go-Fast Ships in Sentinel-1 Imagery
title_sort new automated ship wake detector for small and go fast ships in sentinel 1 imagery
topic remote sensing
SAR
ship detection
wake detection
Sentinel-1
url https://www.mdpi.com/2072-4292/14/24/6223
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