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Article

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities †

1
VRAI Lab, Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
2
CNR-IRBIM, Institute for Marine Biological Resources and Biotechnology, National Research Council, 60125 Ancona, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Galdelli, A.; Mancini, A.; Ferrà, C.; Tassetti, A.N. Integrating AIS and SAR to monitor fisheries: a pilot study in the Adriatic Sea. In Proceedings of the 2020 IMEKO TC-19 International Workshop on Metrology for the Sea, Naples, Italy, 5–7 October 2020; pp. 39–44.
Academic Editor: Gwanggil Jeon
Sensors 2021, 21(8), 2756; https://doi.org/10.3390/s21082756
Received: 26 March 2021 / Revised: 6 April 2021 / Accepted: 10 April 2021 / Published: 13 April 2021
Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas. View Full-Text
Keywords: Automatic Identification System; Synthetic Aperture Radar; data integration; machine learning; maritime surveillance Automatic Identification System; Synthetic Aperture Radar; data integration; machine learning; maritime surveillance
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MDPI and ACS Style

Galdelli, A.; Mancini, A.; Ferrà, C.; Tassetti, A.N. A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities. Sensors 2021, 21, 2756. https://doi.org/10.3390/s21082756

AMA Style

Galdelli A, Mancini A, Ferrà C, Tassetti AN. A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities. Sensors. 2021; 21(8):2756. https://doi.org/10.3390/s21082756

Chicago/Turabian Style

Galdelli, Alessandro, Adriano Mancini, Carmen Ferrà, and Anna N. Tassetti. 2021. "A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities" Sensors 21, no. 8: 2756. https://doi.org/10.3390/s21082756

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