Journal Article

Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea

Tsuda, Masaki E., Park, Jaeyoon, Miller, Nathan A., Oozeki, Yoshioki, Saito, Rui

2023

Overview

Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial intelligence / automation / boats / Boats and boating / Case studies / Cigarette boats / Commercial fishing / Comparative analysis / Data acquisition / Datasets / East China Sea / Fisheries / Fisheries management / Fishing / Identification and classification / Imaging radiometers / Infrared imaging / Infrared radiometers / Learning algorithms / Light / Machine learning / Monitoring / Night / Optical measuring instruments / Performance evaluation / radar / Radiometers / Radiometry / Remote sensing / Remote sensors / Research ships / Stock assessment / Vessels / VIIRS boat detection / VIIRS nightlight