Ship Trajectory Analysis Based on the Integration of Multi-Source Remote Sensing and AIS Data

Abstract Ship trajectories are key to understanding their navigation behavior and maritime activity patterns. To address the limitations of single-source AIS data in trajectory completeness and the constraints of instantaneous observations from remote sensing imagery, this study aims to explore a technical approach for matching multi-source remote sensing data with AIS data to achieve more comprehensive remote sensing identification of ships. This research integrates GF-1/GF-2/Sentinel-2 optical imagery, GF-3/Sentinel-1 SAR imagery, and AIS data. Through strict spatiotemporal matching, ship detection targets in the imagery are associated with AIS vessel identities, forming high-confidence spatiotemporal control points. The study demonstrates that the introduction of multi-source remote sensing data can provide valuable independent verification and visual context for traditional AIS trajectory analysis, enriching single-dimensional trajectories into a three-dimensional behavioral sequence that includes instantaneous scenes. This collaborative analytical framework not only enhances the quality and reliability of trajectory data but also uncovers deeper patterns in ship navigation, providing more robust decision-making support for smart shipping, maritime management, and security assurance.