The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) Glimmer Imager for Urbanization (GIU) data is very sensitive to low radiation and capable of detecting weak light sources from vessels at night while significantly improving the spatial resolution compared to similar products. Most existing methods fail to use the relevant characteristics of vessels effectively, and it is difficult to deal with the complex shape of vessels in high-resolution Nighttime Light (NTL) data, resulting in unsatisfactory detection results. Considering the overall sparse distribution of vessels and the light source diffusion phenomenon, a novel vessel detection method is proposed in this paper, utilizing the high spatial resolution of the SDGSAT-1. More specifically, noise separation is completed based on a local contrast-weighted RPCA. Then, artificial light sources are detected based on a density clustering algorithm, and an inter-cluster merging method is utilized to realize vessel detection further. We selected three research areas, namely, the Bohai Sea, the East China Sea, and the Gulf of Mexico, to establish a vessel dataset and applied the algorithm to the dataset. The results show that the total detection accuracy and the recall rate of the detection algorithm in our dataset are 96.84% and 96.67%, which is significantly better performance than other methods used for comparison in the experiment. The algorithm overcomes the dataset’s complex target shapes and noise conditions and achieves good results, which proves the applicability of the algorithm.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night band (DNB) data is very sensitive to low radiation and capable of detecting faint light sources emitted by vessels at night. Existing vessel detection methods are mainly based on pixel statistical feature estimation or manual experience to determine the threshold for detection. In complex scenes, problems such as interference targets and inaccurate background modeling result in inaccurate detection accuracy and poor robustness. In this paper, a vessel detection method based on an improved constant false alarm rate detector is proposed, and more accurate threshold selection is obtained through background sample truncation and distribution adjustment. Then, the local peak detection algorithm removes the bright spots in the non-vessel location and realizes the vessel detection. We selected data from two research areas, the waters around the East China Sea and the waters around the Gulf of Mexico of the United States, to build a vessel dataset and applied the algorithm to the dataset. The experimental results show that the total detection accuracy of the algorithm is 94.48%, and the recall rate is 93.40%, which is higher than the other two comparison methods. Especially in complex scenarios with high target density, such as ports, the recall rate is significantly improved, which proves the applicability of the algorithm.