Fishing Gear Identification from Vessel-Monitoring-System-based Fishing Vessel Trajectories - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Journal of Oceanic Engineering Année : 2017

Fishing Gear Identification from Vessel-Monitoring-System-based Fishing Vessel Trajectories

Résumé

The surveillance of illegal fishing activities is a critical issue for the management of marine resources. In this study, we investigate the space-based monitoring of fishing vessel activities using VMS (Vessel Monitoring System) trajectory data. Our specific objective is the automatic recognition of the employed fishing gear type from VMS data. The proposed approach combines the extraction of new VMS-derived features, issued from the non- supervised identification and characterization of gear-specific movement patterns, and supervised machine learning, namely RF (Random Forest) and SVM (Support Vector Machine). We explore the use of the proposed features jointly to more classical ones (e.g., mean position and sinuosity index). Overall, we reach recognition performance greater than 97% for the considered Indonesian fisheries and present an application to the detection of abnormal fishing vessel behaviors with respect to the registered fishing gear. We further discuss the relevance of the proposed approach and its potential for the operational monitoring of fishing vessel activities.
Fichier non déposé

Dates et versions

hal-01756098 , version 1 (31-03-2018)

Identifiants

Citer

Marza Ihsan Marzuki, Philippe Gaspar, René Garello, Vincent Kerbaol, Ronan Fablet. Fishing Gear Identification from Vessel-Monitoring-System-based Fishing Vessel Trajectories. IEEE Journal of Oceanic Engineering, 2017, ⟨10.1109/JOE.2017.2723278⟩. ⟨hal-01756098⟩
217 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More