Abstract
Ship classification based on AIS trajectory data is an important aspect of spatio-temporal trajectory data mining. Aiming at the fact that most of the features extracted by the existing ship classification methods are motion features, which ignore the spatial relation between the vessels and the coastline, a Method based on LightGBM (Light Gradient Boosting Machine) for ship classification considering the offshore distance features is proposed. First, the trajectory data is cleaned and segmented, then the basic motion features of the trajectory segment are extracted, and the features are further enhanced by combining the offshore distance of the vessels. After being normalized, the features are filtered by chi-square test method, and finally a LightGBM model is constructed. The selected features are used for classification training of five types of vessels: cargo, passenger, fishing, tug and yacht, and stable classification results are obtained by 5-fold cross-validation. The results show that the average accuracy of ship classification based on LightGBM model is 86.6% after the offshore distance feature is added, and the classification accuracy is improved by 3.1%, which is better than the traditional machine classification methods such as Random Forest, Decision Tree and KNN model.
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Acknowledgements
This project is supported by National Natural Science Foundation of China (41901397, 42101454, 42101455), and supported by Institute of Geospatial Information, University of Information Engineering.
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Xu, L., Chen, X., Wen, B., Ma, J., Wang, Y., Xu, Q. (2023). Ship Classification Based on Trajectories Data and LightGBM Considering Offshore Distance Feature. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_8
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