Ship Type Classification Based on the Ship Navigating Trajectory and Machine Learning

Abstract

Accurate identification of ships is vital to ensure safe maritime activities. As the Automatic Identification System (AIS) provides various static and dynamic information, it has been actively used for ship identification. However, since the ship owner directly enters static information, missing ship-type information may be provided intentionally or unintentionally. In order to solve this problem, it is necessary to devise a new method to classify ship types correctly. So a ship-type classification scheme based on a ship navigating trajectory with AIS data is proposed to solve this problem. First, to acquire training data, historical AIS data provided by the Danish Maritime Authority have been converted into ship trajectories based on the Maritime Mobile Service Identities (MMSI), including corresponding ship types. As one of the main challenges in handling raw datasets is cleaning them to ensure the removal of invalid data, pre-processing is applied. Next, we extracted 54 features, including the behavior and shape of the overall trajectory. We especially proposed new features that could represent the shape of the overall trajectory using shape-based features designed for sketch recognition. Based on the extracted features, several benchmark classification algorithms are trained to classify four types of ships: fishing, passenger, tanker, and cargo. Finally, we check which features are valuable for recognizing ship types with feature selection and which models can implement good performance in ship classification through performance analysis. The results demonstrate that the shape-based features designed for sketch recognition could express essential characteristics of ship trajectories and could be used for ship classification. Furthermore, Random Forest performs better than other classifiers, and the classification accuracy of the four types of ships could reach 89.44%