Vessel Target Detection Method Based on an Improved CFAR Method in Nighttime Remote Sensing Images

The Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) data are 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 article, a vessel detection method based on an improved constant false alarm rate (CFAR) 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 nonvessel 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.