快照视角下的船舶群体运动模式识别算法  

A Methodology of Vessel Group Motion Pattern Recognition in Snapshot Perspective

作　　者：周宇氚 李连营[1] 张翔[2,3,4] ZHOU Yuchuan;LI Lianying;ZHANG Xiang(School of Resources and Environmental Sciences,Wuhan University,Wuhan 430079,China;School of Geosptial Engineering and Science,Sun Yat‐Sen University,Zhuhai 519082,China;Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China;Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat‐sen University),Ministry of Education,Zhuhai 519082,China)

机构地区：[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]中山大学测绘科学与技术学院,广东珠海519082 [3]南方海洋科学与工程广东省实验室(珠海),广东珠海519082 [4]极地环境立体观测与应用教育部重点实验室(中山大学),广东珠海519082

出　　处：《测绘地理信息》2024年第6期40-45,共6页Journal of Geomatics

基　　金：国家自然科学基金(42271458,41671384)

摘　　要：从时间快照视角提出了一个群体识别算法框架。首先,讨论了船舶群体的概念,并结合群体动力学和视觉感知相关理论,定量化了基本群体模式(如直线、曲线和流)的识别规则。接着,利用苏伊士运河地区的船舶AIS(automatic identificationsystem)数据进行了案例研究。通过与DBSCAN(Density-Based Spatial Clustering of Applications with Noise)和局部方向中心性算法(Clustering by measuring local direction centrality,CDC)进行对比,本文方法在识别细粒度船舶群方面表现更优。In this study,we propose a framework for group recognition algorithms from a time snapshot perspective.First,we discuss the concept of vessel groups and quantify the recognition rules for basic group patterns(e.g.,collinear,curvilinear and flow)by combining perception theory and group dynamics theory.Next,we conducted a case study using automatic identification system(AIS)data from the Suez Canal area.Compared with DBSCAN(density-based spatial clustering of applications with noise)and CDC(clustering by measuring local direction centrality),the method proposed in this study performs better in identifying fine-grained ship clusters.

关 键 词：聚类 海上交通 群体行为 

分 类 号：U675.7[交通运输工程—船舶及航道工程]