Multimodal fusion via ship trajectory understanding for cognitive maritime intelligence: A case study of the Fujian sea

doi:10.1016/j.oceaneng.2026.124237

CSpace

	Multimodal fusion via ship trajectory understanding for cognitive maritime intelligence: A case study of the Fujian sea
	Li, Yong 1; Bai, Yundi 1; Mei, Qiang 2,3; Wang, Peng 3,4; Hu, Yu 5; Yan, Qiaoling 6; Wang, Shaohua 7
	2026-03-30
发表期刊	OCEAN ENGINEERING
ISSN	0029-8018
卷号	350 页码:20
摘要	The maritime traffic in coastal areas is characterized by high complexity, and certain vessels frequently alter their static Automatic Identification System (AIS) data to evade regulatory oversight. This practice leads to inaccuracies and omissions in vessel tracking, making it essential to classify vessel types through a multidimensional analysis integrating diverse information sources and advanced artificial intelligence techniques. However, the direct application of deep learning models to maritime vessel trajectory data is hindered by high dimensionality, sparsity, and inherent noise, which impede semantic and spatial information extraction and often result in suboptimal classification. To overcome these limitations, this study proposes a grid-based maritime trajectory classification model that leverages cross-modal fusion. By integrating joint text-image bimodal modeling with a novel dynamic alignment mechanism, the proposed approach enables real-time guidance of local features through semantic information. Specifically, AIS trajectories are discretized into grid-based text sequences and trajectory images. Textual features are extracted using a Bidirectional Long Short-Term Memory network, while visual representations are processed by a Dilated Residual Network. A dynamic alignment module then adaptively modulates image features with semantic information from the text encoder, enhancing deep cross-modal interactions. Experimental validation on real-world AIS datasets of Fujian sea demonstrates that the proposed model outperforms seven unimodal baselines and five cross-modal models, consistently achieving a 2% improvement in both classification accuracy and F1-score among cross-modal approaches. Systematic ablation studies further confirm the effectiveness of the feature fusion mechanism. In summary, this research advances maritime domain awareness by enabling more accurate and robust vessel trajectory classification. The proposed framework offers significant potential for monitoring illegal vessel activities, contributing to improved maritime security and operational oversight for shipping management.
关键词	AIS big data Trajectory classification Cross-modal fusion Multidimensional analysis Fujian sea
DOI	10.1016/j.oceaneng.2026.124237
收录类别	SCI
语种	英语
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
WOS记录号	WOS:001672617700001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42858
专题	中国科学院计算技术研究所
通讯作者	Mei, Qiang; Wang, Shaohua
作者单位	1.Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China 2.Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China 3.Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 5.China Univ Geosci, Wuhan 430074, Peoples R China 6.Xiamen Univ Technol, Sch Optoelect & Commun Engn, Xiamen 361021, Peoples R China 7.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Li, Yong,Bai, Yundi,Mei, Qiang,et al. Multimodal fusion via ship trajectory understanding for cognitive maritime intelligence: A case study of the Fujian sea[J]. OCEAN ENGINEERING,2026,350:20.
APA	Li, Yong.,Bai, Yundi.,Mei, Qiang.,Wang, Peng.,Hu, Yu.,...&Wang, Shaohua.(2026).Multimodal fusion via ship trajectory understanding for cognitive maritime intelligence: A case study of the Fujian sea.OCEAN ENGINEERING,350,20.
MLA	Li, Yong,et al."Multimodal fusion via ship trajectory understanding for cognitive maritime intelligence: A case study of the Fujian sea".OCEAN ENGINEERING 350(2026):20.