Ship Classification Based on AIS Data and Machine Learning Methods

AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus mak...

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Bibliographic Details
Published in:Electronics (Basel) 2024-01, Vol.13 (1), p.98
Main Authors: Huang, I-Lun, Lee, Man-Chun, Nieh, Chung-Yuan, Huang, Juan-Chen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automatic classification
Cargo ships
Classification
Classifiers
Container ships
Containers
Fishing vessels
Human error
Identification
Impact acceleration
Machine learning
Monitoring
Neural networks
Offshore
Satellites
Ship accidents & safety
Shipping industry
Ships
Steering
Surveillance
Wind farms
Wind power
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Summary:AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13010098