Abstract
It is important for maritime authorities to effectively identify and classify unknown types of ships in historical trajectory data. A method of using trajectory image and training based on deep residual network (ResNet) to obtain ship type classifier is proposed. First, a method of integrating speed information into the trajectory image is proposed, then the trajectory image is input into the ResNet model for training to obtain the classifier. Finally, the real AIS data of 5 ship types are used for experiments. The experimental results show that the method can meet the requirements of type identification and classification of ships based on AIS data, and provides technical support for further research on the identification of camouflaged ships, mining of ship behavior patterns and anomaly detection.
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Luo, P., Gao, J., Wang, G., Han, Y. (2021). Research on Ship Classification Method Based on AIS Data. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_17
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DOI: https://doi.org/10.1007/978-981-16-2540-4_17
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