Abstract
Many ships have AIS devices that can provide information such as the types of ships, which can help the maritime authorities manage the marine traffic in a better way. However, when some ships do not have AIS devices installed or turn off these devices, it is desirable to identify the types of ships by the trajectory features provided by radar. In order to achieve this goal, first, the trajectories are generated based on the obtained AIS points and radar points, and then the radar trajectories are associated with the AIS trajectories to obtain the labels of the radar trajectories. Next, three types of features of radar trajectories are extracted. Due to the small amount of experimental data and the problem of class imbalance, this paper proposes a heterogeneous ensemble learning method based on EasyEnsemble and SMOTE when training the ship classification model. The experimental results show that the proposed method is superior to homogeneous ensemble learning and heterogeneous ensemble learning without SMOTE methods. Moreover, the method can identify almost all the minority class samples and has certain application value.
This work has been supported by Beijing Natural Science Foundation (Grant No. 4182042), National Key Research and Development Program of China (No. 2018YFB1003804).
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Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Habtemariam, B.K., Tharmarasa, R., Meger, E., Kirubarajan, T.: Measurement level AIS/radar fusion for maritime surveillance. In: Signal and Data Processing of Small Targets 2012, vol. 8393, p. 83930I. International Society for Optics and Photonics (2012)
Ji, Q., Jin, B., Cui, Y., Zhang, F.: Using mobile signaling data to classify vehicles on highways in real time. In: 2017 18th IEEE International Conference on Mobile Data Management (MDM), pp. 174–179. IEEE (2017)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning Proceedings 1992, pp. 249–256. Elsevier (1992)
Kraus, P., Mohrdieck, C., Schwenker, F.: Ship classification based on trajectory data with machine-learning methods. In: 2018 19th International Radar Symposium (IRS), pp. 1–10. IEEE (2018)
Krüger, M.: Experimental comparison of ad hoc methods for classification of maritime vessels based on real-life AIS data. In: 2018 21st International Conference on Information Fusion (FUSION), pp. 1–7. IEEE (2018)
Liu, T.Y.: Easyensemble and feature selection for imbalance data sets. In: 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, pp. 517–520. IEEE (2009)
Ljunggren, H.: Using deep learning for classifying ship trajectories. In: 2018 21st International Conference on Information Fusion (FUSION), pp. 2158–2164. IEEE (2018)
McCauley, D.J., et al.: Ending hide and seek at sea. Science 351(6278), 1148–1150 (2016)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Sheng, K., Liu, Z., Zhou, D., He, A., Feng, C.: Research on ship classification based on trajectory features. J. Navig. 71(1), 100–116 (2018)
Urbanowicz, R.J., Moore, J.H.: ExSTraCS 2.0: description and evaluation of a scalable learning classifier system. Evol. Intel. 8(2–3), 89–116 (2015)
Xia, H., Qiao, Y., Jian, J., Chang, Y.: Using smart phone sensors to detect transportation modes. Sensors 14(11), 20843–20865 (2014)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 312–321. ACM (2008)
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Zhang, T., Zhao, S., Chen, J. (2019). Research on Ship Classification Based on Trajectory Association. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_28
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DOI: https://doi.org/10.1007/978-3-030-29551-6_28
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