TY  - JOUR
AU  - Liu, Jun
AU  - Chen, Zhen
AU  - Zhou, Jihao
AU  - Xue, Anke
AU  - Peng, Dongliang
AU  - Gu, Yu
AU  - Chen, Huajie
PY  - 2024
DA  - 2024/05/25
TI  - Research on A Ship Trajectory Classification Method Based on Deep Learning
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 1
IS  - 1
SP  - 3
EP  - 15
DO  - 10.62762/CJIF.2024.361873
UR  - https://www.icck.org/article/abs/CJIF.2024.361873
KW  - deep learning
KW  - trajectory classification
KW  - AIS data
KW  - data fusion
KW  - ship monitoring
AB  - 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.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Liu2024Research,
  author = {Jun Liu and Zhen Chen and Jihao Zhou and Anke Xue and Dongliang Peng and Yu Gu and Huajie Chen},
  title = {Research on A Ship Trajectory Classification Method Based on Deep Learning},
  journal = {Chinese Journal of Information Fusion},
  year = {2024},
  volume = {1},
  number = {1},
  pages = {3-15},
  doi = {10.62762/CJIF.2024.361873},
  url = {https://www.icck.org/article/abs/CJIF.2024.361873},
  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.},
  keywords = {deep learning, trajectory classification, AIS data, data fusion, ship monitoring},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}