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An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation
Liang, Maohan1,2; Liu, Ryan Wen1,2; Li, Shichen3; Xiao, Zhe4; Liu, Xin5; Lu, Feng2
2021-04-01
Source PublicationOCEAN ENGINEERING
ISSN0029-8018
Volume225Pages:16
Corresponding AuthorLiu, Ryan Wen(wenliu@whut.edu.cn) ; Lu, Feng(luf@lreis.ac.cn)
AbstractTo achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel trajectory similarity has recently attracted increasing attention in the maritime data mining research community. However, traditional shape- and warping-based methods often suffer from several drawbacks such as high computational cost and sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To eliminate these drawbacks, we propose an unsupervised learning method which automatically extracts low-dimensional features through a convolutional auto-encoder (CAE). In particular, we first generate the informative trajectory images by remapping the raw vessel trajectories into two-dimensional matrices while maintaining the spatio-temporal properties. Based on the massive vessel trajectories collected, the CAE can learn the low-dimensional representations of informative trajectory images in an unsupervised manner. The trajectory similarity is finally equivalent to efficiently computing the similarities between the learned low-dimensional features, which strongly correlate with the raw vessel trajectories. Comprehensive experiments on realistic data sets have demonstrated that the proposed method largely outperforms traditional trajectory similarity computation methods in terms of efficiency and effectiveness. The high-quality trajectory clustering performance could also be guaranteed according to the CAE-based trajectory similarity computation results.
KeywordAutomatic identification system (AIS) Trajectory similarity Trajectory clustering Convolutional neural network (CNN) Convolutional auto-encoder (CAE)
DOI10.1016/j.oceaneng.2021.108803
Indexed BySCI
Language英语
Funding ProjectState Key Laboratory of Resources and Environmental Information System ; National Key R&D Program of China[2018YFC1407404]
Funding OrganizationState Key Laboratory of Resources and Environmental Information System ; National Key R&D Program of China
WOS Research AreaEngineering ; Oceanography
WOS SubjectEngineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
WOS IDWOS:000631882800024
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/162123
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Ryan Wen; Lu, Feng
Affiliation1.Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
3.Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430063, Peoples R China
4.ASTAR, Inst High Performance Comp, CO, Singapore 118411, Singapore
5.AIST, AIRC RWBC OIL, Tokyo 1350064, Japan
Recommended Citation
GB/T 7714
Liang, Maohan,Liu, Ryan Wen,Li, Shichen,et al. An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation[J]. OCEAN ENGINEERING,2021,225:16.
APA Liang, Maohan,Liu, Ryan Wen,Li, Shichen,Xiao, Zhe,Liu, Xin,&Lu, Feng.(2021).An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation.OCEAN ENGINEERING,225,16.
MLA Liang, Maohan,et al."An unsupervised learning method with convolutional auto-encoder for vessel trajectory similarity computation".OCEAN ENGINEERING 225(2021):16.
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