基于轨迹图片特征距离的船舶轨迹聚类OACSTPCD

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.