Enhancing Detection of Nighttime Fishing Boat Lights Using VIIRS Satellite Data and Deep Neural Networks - Suk Yoon; Kwang-Seok Kim; Hee-Jeong Han; Hyeong-Tak Lee; Hyun Yang (Advances in Technology Innovation, 2026)
Illegal nighttime fishing remains difficult to monitor because vessels often deactivate the Automatic Identification System (AIS). This study presents a supervised deep neural network (DNN) approach for detecting nighttime fishing vessel lights using the Day/Night Band (DNB) data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP and NOAA-20 satellites. The model integrates DNB radiance features with lunar illumination information to reduce false detections caused by moonlight. The dataset comprises summer-season observations (2020–2021) in waters near Jeju Island, South Korea, with labels derived from temporally matched AIS records. The proposed DNN is evaluated using a stratified train–test split and compared with conventional machine-learning baselines. Experimental results demonstrate improved performance, achieving an F1 score above 0.90, indicating the robust detection capability under low-light maritime conditions. These findings highlight the potential of VIIRS DNB data combined with deep learning for large-scale nighttime maritime monitoring beyond AIS-dependent systems.