Spatiotemporal Point–Trace Matching Based on Multi-Dimensional Feature Fuzzy Similarity Model

Abstract

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.