Research on A Ship Trajectory Classification Method Based on Deep Learning

Jun Liu; Zhen Chen; Jihao Zhou; Anke Xue; Dongliang Peng; Yu Gu; Huajie Chen

doi:10.62762/CJIF.2024.361873

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Volume 1, Issue 1, Chinese Journal of Information Fusion

Volume 1, Issue 1, 2024

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Tsinghua University, China

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Chinese Journal of Information Fusion, 2024, Volume 1, Issue 1: 3-15

Free Access | Research Article | 25 May 2024 | Cited: 3

Research on A Ship Trajectory Classification Method Based on Deep Learning

Jun Liu 1 *

Zhen Chen 1

Jihao Zhou 1

Anke Xue 1

Dongliang Peng 1

Yu Gu 1

Huajie Chen 1

1 School of Automation, Hangzhou Dianzi University, Zhejiang 310018, China

* Corresponding author: Jun Liu, email: junliu@hdu.edu.cn

DOI: 10.62762/CJIF.2024.361873

Received: 19 February 2024, Accepted: 20 May 2024, Published: 25 May 2024

Abstract

The unrestricted development and utilization of marine resources have resulted in a series of practical problems, such as the destruction of marine ecology. The wide application of radar, satellites and other detection equipment has gradually led to a large variety of large-capacity marine spatiotemporal trajectory data from a vast number of sources. In the field of marine domain awareness, there is an urgent need to use relevant information technology means to control and monitor ships and accurately classify and identify ship behavior patterns through multisource data fusion analysis. In addition, the increase in the type and quantity of trajectory data has produced a corresponding increase in the complexity and difficulty of data processing that cannot be adequately addressed by traditional data mining algorithms. Therefore, this paper provides a deep learning-based algorithm for the recognition of four main motion types of the ship from automatic identification system (AIS) data: anchoring, mooring, sailing and fishing. A new method for classifying patterns is presented that combines the computer vision and time series domains. Experiments are carried out on a dataset constructed from the open AIS data of ships in the coastal waters of the United States, which show that the method proposed in this paper achieves more than 95\% recognition accuracy. The experimental results confirm that the method proposed in this paper is effective in classifying ship trajectories using AIS data and that it can provide efficient technical support for marine supervision departments.

Graphical Abstract

Keywords

Deep learning

Trajectory Xlassification

AIS Data

Data Fusion

Ship Monitoring

References

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APA Style

Liu, J., Chen, Z., Zhou, J., Xue, A., Peng, D., Gu, Y., & Chen, H. (2024). Research on A Ship Trajectory Classification Method Based on Deep Learning. Chinese Journal of Information Fusion, 1(1), 3–15. https://doi.org/10.62762/CJIF.2024.361873

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