中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification

文献类型：期刊论文

作者Sun, Tao1,2; Xu, Yongjun1; Zhang, Zhao1; Wu, Lin1; Wang, Fei1
刊名SENSORS
出版日期2022-02-01
卷号22期号:3页码:16
关键词ship classification spatial-temporal embedding feature enhancement deep learning attention
DOI10.3390/s22030711
英文摘要Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points' semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available.
资助项目NSFC[61902376] ; National Key Research and Development Program of China[2018YFC1407400]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000754840800001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/18966]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Fei
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100039, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Tao,Xu, Yongjun,Zhang, Zhao,et al. A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification[J]. SENSORS,2022,22(3):16.
APA Sun, Tao,Xu, Yongjun,Zhang, Zhao,Wu, Lin,&Wang, Fei.(2022).A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification.SENSORS,22(3),16.
MLA Sun, Tao,et al."A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification".SENSORS 22.3(2022):16.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。