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

Abstract

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.

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

“…Synthetic aperture radar (SAR) and optical imagery from satellites facilitate a multitude of image-processing techniques for detecting and classifying vessels in the images [32][33][34], including identification of fishing vessels [12] and fishing activity [35]. Lights used by some fisheries to attract target species can also be detected using the visible infrared imaging radiometer suite (VIIRS) and used to map the spatial and temporal extent of activity [35,36] and identify discrete vessel positions [37,38]. However, the temporal lag of vessel detections across subsequent images complicates route prediction and enforcement response on the water.…”

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

“…Currently, NTL satellite-based vessel detection methods can be broadly classified into two types, namely, threshold-based methods [13][14][15] and machine learning approaches [16,17] The predominant focus of threshold-based research for identifying illuminated vessels centers on leveraging the significant radiometric contrast between illuminated vessels and the oceanic backdrop. For instance, Elvidge et al [13] conducted a detailed analysis of spike features within VIIRS/DNB data, resulting in the identification of vessel detections that include strong, weak, and diffused signals, eliminating non-targeted noise from cloud-scattered light.…”

“…Shao et al [16] introduced an improved version of the TASFF-YOLOv5 algorithm, leading to enhanced feature fusion and superior results on a vessel dataset they constructed. In a different approach, Tsuda et al [17] first used a two-step training method for their machine learning model, which is designed to mitigate the generation of a large number of false positives and achieve continuous and frequent detections, especially after excluding various cloud and moon conditions. The aforementioned studies illustrate that deep learning methods can markedly enhance the efficiency of vessel detection, offering a new paradigm and direction for night-time vessel detection.…”