Extracting ship and heading from Sentinel-2 images using convolutional neural networks with point and vector learning

Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications. However, current deep learning-based methods primarily focus on ship position detection, while the detection of ship wakes relies on traditional non-deep learning approaches, which often underperform in complex marine environments. We proposed a novel, simple, and efficient method called Point-Vector Net. The proposed method leverages convolutional neural networks (CNN) for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps. In the final stage, ship positions and headings are represented using a combination of points and vectors. Comparative experiments with results from automatic identification system (AIS) reports demonstrate that our method achieved impressive performance in two-class ship target detection, with an average precision of 96.4%, recall rate of 94.3%, and an F1 score of 95.2%. Notably, the average heading error was 3.3°. The proposed model achieved a practical inference speed (FPS>30), and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.