Ship Classification Method for Massive AIS Trajectories Based on GNN

Abstract

Since criminals and maritime terrorism may tamper with AIS data and make the track suspicious, it is urgent to classify ships accurately and improve maritime navigation safety. Ship classification based on trajectory data can make up for the deficiency of traditional radar identification and optical identification which has important academic significance and practical value. The target recognition technology based on the traditional neural network can only process conventional Euclidean structure data, while the emerging graph neural network shows great advantages in processing non-Euclidean structure data. The ship trajectory data has the characteristics of the time and space domain and shows a non-Euclidean structure; therefore this paper proposes a classification and recognition method based on the graph neural network to process ship AIS data. First of all, the ship trajectory data is preprocessed and converted into graph data with vertices and edges. Then we use GNN to classify 4 types of ships including fishing vessels, passenger ships, oil tankers, and container ships. Finally, we compare the results with the SVM method. And it shows that this method is valid and proves that it is an effective method of ship classification.