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Research on Ship Classification Based on Trajectory Features

Published online by Cambridge University Press:  23 August 2017

Kai Sheng ,
Zhong Liu ,
Dechao Zhou ,
Ailin He  and
Chengxu Feng
Kai Sheng*
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Zhong Liu
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Dechao Zhou
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Ailin He
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Chengxu Feng
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
*
(E-mail: [email protected])
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Abstract

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

Keywords

Ship classificationTrajectory feature extractionAIS dataLogistic regression
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 100 - 116
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Aarsæther, K.G. and Moan, T. (2009). Estimating navigation patterns from AIS. Journal of Navigation, 62(4), 587–607.CrossRefGoogle Scholar
Boukhechba, M., Bouzouane, A., Bouchard, B., Gouinvallerand, C. and Giroux, S. (2015). Online recognition of people's activities from raw GPS data: semantic trajectory data analysis. ACM International Conference on Pervasive Technologies Related to Assistive Environments, 1–8.Google Scholar
De Vries, G.K.D. and Van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems with Applications, 39(18), 13426–13439.CrossRefGoogle Scholar
De Vries, G.K.D. and Van Someren, M. (2014). An analysis of alignment and integral based kernels for machine learning from vessel trajectories. Expert Systems with Applications, 41(16), 7596–7607.CrossRefGoogle Scholar
Dodge, S., Weibel, R. and Forootan, E. (2009). Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Computers Environment & Urban Systems, 33(6), 419–434.CrossRefGoogle Scholar
Elwakdy, M., El-Bendary, M. and Eltokhy, M. (2015). A Novel Trajectories Classification Approach for different types of ships using a Polynomial Function and ANFIS. International Conference on Image Processing, Computer Vision, & Pattern Recognition.Google Scholar
Feng, Z. and Zhu, Y. (2016). A survey on trajectory data mining: techniques and applications. IEEE Access, 4, 1–1.CrossRefGoogle Scholar
Harrington, P. (2012). Machine Learning in Action. Manning Publications Co., LTD.Google Scholar
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. Springer.CrossRefGoogle Scholar
Kotsiantis, S.B. (2007). Supervised machine learning: a review of classification techniques. Informatica, 31(3), 249–268.Google Scholar
Lee, J.G., Han, J. and Whang, K.Y. (2007). Trajectory clustering: a partition-and-group framework. ACM SIGMOD International Conference on Management of Data, pp. 593–604.Google Scholar
Lee, J.G., Han, J., Li, X. and Gonzalez, H. (2008). TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proceedings of the VLDB Endowment, 1(1), 1081–1094.CrossRefGoogle Scholar
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W. and Ma, W.Y. (2008). Mining user similarity based on location history. ACM Sigspatial International Symposium on Advances in Geographic Information Systems, ACM-GIS 2008, California, USA.Google Scholar
McCauley, D.J., Woods, P., Sullivan, B., Bergman, B. Jablonicky, C., Roan, A., Hirshfield, M., Boerder, K. and Worm, B. (2016). Ending hide and seek at sea. Science, 351(6278), 1148–1150.CrossRefGoogle ScholarPubMed
Patel, D. (2013). Incorporating duration and region association information in trajectory classification. Journal of Location Based Services, 7(4), 246–271.CrossRefGoogle Scholar
Patel, D., Sheng, C., Hsu, W. and Lee, M.L. (2012). Incorporating Duration Information for Trajectory Classification. IEEE International Conference on Data Engineering, 41, 1132–1143.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V. and Thirion, B. (2012). Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12(10), 2825–2830.Google Scholar
Schein, A.I. and Ungar, L.H. (2007). Active learning for logistic regression: an evaluation. Machine Learning, 68(3), 235–265.CrossRefGoogle Scholar
Scikit-learn User Guide. (2017). Logistic Regression. http://scikit-learn.org/stable/modules/linear_model.html#; logsitic-regression. Accessed 11 May 2017.Google Scholar
Silveira, P.A.M., Teixeira, A.P. and Guedes Soares, C. (2013). Use of AIS Data to Characterize Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. Journal of Navigation, 66(6), 879–898.CrossRefGoogle Scholar
Wang, Y., Zheng, Y. and Xue, Y. (2014). Travel time estimation of a path using sparse trajectories. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 25–34.CrossRefGoogle Scholar
Yuan, N.J., Zheng, Y., Zhang, L. and Xie, X. (2013). T-finder: a recommender system for finding passengers and vacant taxis. IEEE Transactions on Knowledge & Data Engineering, 25(10), 2390–2403.CrossRefGoogle Scholar
Zhen, R., Jin, Y., Hu, Q., Shao, Z. and Nikitakos, N. (2017). Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier. Journal of Navigation. 70(3), 648–670.CrossRefGoogle Scholar
Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems & Technology, 6(3), 1–41.CrossRefGoogle Scholar
Zhou, Z. (2016). Machine Learning (in Chinese). Tsinghua University press.Google Scholar
Zhu, F., Zhang, Y. and Gao, Z. (2012). Research on Ship Behaviors Based on Data Mining (in Chinese). Navigation of China, 35(2), 50–54.Google Scholar
Zhu, Y., Zheng, Y., Zhang, L., Santani, D., Xie, X. and Yang, Q. (2011). Inferring taxi status using GPS trajectories. Technical Report MSR-TR-2011-144.Google Scholar