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Ship Detection Based on Faster R-CNN using Range-Compressed Airborne Radar Data

Loran, Tamara and Barros Cardoso da Silva, Andre and Joshi, Sushil Kumar and Baumgartner, Stefan V. and Krieger, Gerhard (2022) Ship Detection Based on Faster R-CNN using Range-Compressed Airborne Radar Data. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2022.3229141. ISSN 1545-598X.

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Official URL: https://ieeexplore.ieee.org/document/9984160


Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne range-compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors are tested on multiple scenarios, showing high recall performance of the models even in very dense multi-target scenarios in the complex inshore environment of the North Sea.

Item URL in elib:https://elib.dlr.de/189078/
Document Type:Article
Title:Ship Detection Based on Faster R-CNN using Range-Compressed Airborne Radar Data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Barros Cardoso da Silva, AndreUNSPECIFIEDhttps://orcid.org/0000-0001-5056-4013UNSPECIFIED
Joshi, Sushil KumarUNSPECIFIEDhttps://orcid.org/0000-0002-4494-5255UNSPECIFIED
Baumgartner, Stefan V.UNSPECIFIEDhttps://orcid.org/0000-0002-8337-6825UNSPECIFIED
Krieger, GerhardUNSPECIFIEDhttps://orcid.org/0000-0002-4548-0285UNSPECIFIED
Date:14 December 2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Moving target indication (MTI), synthetic aperture radar (SAR), airborne radar, maritime safety, deep learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - V&V4NGC - Methoden, Prozesse und Werkzeugketten für die Validierung & Verifikation von NGC
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute
Deposited By: Barros Cardoso da Silva, Andre
Deposited On:15 Dec 2022 11:07
Last Modified:28 Jun 2023 11:41

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