Recognizing fishing activities via VMS trace analysis based on Mathematical Morphology

Recently, the satellite-based Vessel Monitoring Systems (VMS) have been widely deployed on fishing vessels. Recognition of fishing activity is the key task for various applications. Previous approaches are basically according to change of vessel's speed; or rely on the validated data from logbooks or documented observations. In this paper, with a rated 60-second temporal resolution VMS data for two years on 34 vessels (typed as otter trawl) in the East China Sea, we propose a Fishing Activity Recognition system (FAR). It exploits Mathematical Morphology for analyzing the VMS trace data to recognize fishing activities and obtain fishing related metrics. Different from previous approaches, FAR carries out on VMS traces only, requiring no other reference like logbooks or any documented observations.