Journal of Dalian Maritime University >
2018 , Vol. 44 >Issue 3: 15 - 21
DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2018.03.003
Ship trajectory classification algorithm based on KNN
Received date: 2018-01-20
Revised date: 2018-02-26
Online published: 2024-06-27
A ship trajectory classification algorithm based on KNN (K-Nearest Neighbor) was proposed. The average distance, speed distance and heading distance were fused between trajectories to form the integrated distance. By initial clustering of ship trajectories, the trajectory of KNN classification samples was obtained. Taking the integrated distance as the distance between trajectories in KNN classification, trajectories classification was thus achieved. Case study was performed to validate the feasibility of proposed algorithm by using AIS data of Wuhan reach of the Yangtze River in May 2017, and ship trajectory classification verification was carried out based on the mean distance between tracks, Hausdorff distance and the integrated distance. Results indicate that when more trajectories points are engaged, the average distance between the tracks is better than the Hausdorff distance, and the KNNbased classification method has better experimental results, which can be used in the actual ship trajectory classification.
LIU Lei , CHU Xiu-min , JIANG Zhong-lian , ZHONG Cheng , ZHANG Dai-yong . Ship trajectory classification algorithm based on KNN[J]. Journal of Dalian Maritime University, 2018 , 44(3) : 15 -21 . DOI: 10.16411/j.cnki.issn1006-7736.2018.03.003
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