A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification

doi:10.3390/s22030711

	A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification
	Sun, Tao 1,2; Xu, Yongjun 1; Zhang, Zhao 1; Wu, Lin 1; Wang, Fei 1
	2022-02-01
发表期刊	SENSORS
卷号	22 期号:3 页码:16
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.
关键词	ship classification spatial-temporal embedding feature enhancement deep learning attention
DOI	10.3390/s22030711
收录类别	SCI
语种	英语
资助项目	NSFC[61902376] ; National Key Research and Development Program of China[2018YFC1407400]
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000754840800001
出版者	MDPI
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18967
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.