舰船电子工程2024，Vol.44Issue(6)：30-35,6.DOI:10.3969/j.issn.1672-9730.2024.06.007
基于轨迹图片特征距离的船舶轨迹聚类
Ship Trajectory Clustering Based on Image Feature Distance
摘要
Abstract
In order to further optimize the management of maritime traffic,a ship trajectory clustering algorithm based on dis-tance metrics of trajectory image features is proposed.This algorithm aims to address the problems of difficult setting of weight pa-rameters and long running time for traditional ship trajectory clustering algorithms based on multiple dimensional attributes.The al-gorithm utilizes Automatic Identification System(AIS)data to draw trajectory images based on the position,speed,and course of trajectory points.The trajectory image features are extracted via a deep residual network trained on large-scale image data.The fea-ture dimensionality is reduced via principal component analysis.The distance measure between trajectories is based on the Euclide-an distance of feature vectors.The density-based noise-tolerant clustering algorithm(DBSCAN)is employed to cluster the reduced ship trajectory image features.Experiment results show that the proposed algorithm can effectively cluster the trajectories while re-ducing the running time.The characteristics of the ship traffic flow reflected by the trajectory clusters are consistent with the actual situation.关键词
船舶轨迹聚类/船舶轨迹距离度量/DBSCAN/船舶交通流特征Key words
ship trajectory clustering/ship trajectory distance measure/DBSCAN/characteristics of the ship traffic flow分类
信息技术与安全科学引用本文复制引用
史祺,范亚琼,张丹普,杨剑锋..基于轨迹图片特征距离的船舶轨迹聚类[J].舰船电子工程,2024,44(6):30-35,6.基金项目
国家重点研发计划(编号:2020YFC0833406)资助. （编号:2020YFC0833406）
