Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea

• Main Authors: Boris Snapir, Toby W. Waine, Lauren Biermann
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Remote Sensing
• Subjects: fisheries; vessel classification; Sentinel-1; Machine Learning; AIS
• Online Access: https://www.mdpi.com/2072-4292/11/3/353

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel <i>detection</i> on satellite imagery, but research on vessel <i>classification</i> has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel&#8217;s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (<i>am</i> or <i>pm</i>). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem.