Yoon, S.; Lee, H.-T.; Kim, M.-K.; Choi, H.-M.; Lee, J.-S.; Han, H.-J., and Yang, H., 2023. Night fishing boat light detection using DNB data and deep neural network. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 220-224. Charlotte (North Carolina), ISSN 0749-0208.

Recently, illegal fishing problems have occurred frequently in the coastal zones near the Korean Peninsula. In particular, it is more difficult to detect and cracking down the illegal fishing boats at night because they deactivate the Automatic Identification System (AIS) transmitter. Meanwhile, The NOAA-20 (JPSS-1) satellite which is equipped with the Suomi National Polar-orbiting (S-NPP) sensor was launched on November 18, 2017. Day/Night Band (DNB) data from Visible Infrared Imaging Radiometer Suite (VIIRS) of S-NPP can be used to detect low illumination light. In this study, therefore, first, we focused on presenting the method to produce DNB data from S-NPP VIIRS using TeraScan software and Raw Data Records (RDR), Science Data Records (SDR), and Environmental Data Record (EDR) data. Then we proposed the Deep Neural Network (DNN) based model to detect night fishing boat lights using the DNB data, the difference between new and full moonlight, and the cloud information. It is expected that this study will contribute to monitor the distribution of fishing boats at night and protect the fishing ground near the Korean Peninsula.