Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, P_d, F_a, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

The South China Sea (SCS) is an important region of fishery resources. However, its fishery resources have been threatened, mainly because of overfishing. In this study, we explored the distribution of night-time fishing boats and analyzed the relationship between fishing activities and marine environmental factors in the northwestern SCS (NWSCS). Firstly, the spatiotemporal variations in nighttime fishing boats in each month of 2021 in the NWSCS were studied. Meanwhile, a fishery activity center index was used to analyze the overall fishery activity trend in the NWSCS. Finally, the spatiotemporal distribution patterns of corresponding environmental factors (i.e., Chl-a, SSS, SST, latitude, longitude) were analyzed, and the nonlinear relationship between environmental factors and fishery activities was quantitatively studied using the generalized additive model. The results showed that fishery activities were mainly distributed in the waters of Beibu Gulf and the southwest of Hainan Island. Meanwhile, there were obvious seasonal differences (i.e., trimodal distribution) in the intensity of fishery activities in the NWSCS. Chl-a was the most important impact factor with a contribution of 21.7%, followed by SSS, longitude, SST, and latitude, with contributions of 12.8%, 9.4%, 4.2%, and 0.5%, respectively. Fishery activities in the NWSCS were mainly distributed in the area with Chl-a of 0~0.35 mg/m³, SST of 21.2~26.4 °C, and SSS of 32.9~33.8 Practical Salinity Unit. This study reveals that more efforts are required to prevent IUU fishing activities for the sustainable development of marine ecosystems in the NWSCS. It is also necessary to improve remote sensing technology to support making sustainable fishing plans.