A semi-supervised deep learning approach for vessel trajectory classification based on AIS data. (1st March 2022)

• Record Type: Journal Article
• Authors: Duan, Hongda; Ma, Fei; Miao, Lixin; Zhang, Canrong
• Is Part Of: Ocean & coastal management. Volume 218(2022)
• Publication Date: 2022-03-01
• Subjects: Automatic identification system -- Vessel trajectory classification -- Deep learning -- Semi-supervised learning -- Variational autoencoder; Marine resources -- Management -- Periodicals; Coastal zone management -- Periodicals; Coastal ecology -- Periodicals; Ressources marines -- Périodiques; Littoral -- Aménagement -- Périodiques; Écologie littorale -- Périodiques; Coastal ecology; Coastal zone management; Marine resources -- Management; Periodicals; Electronic journals; 551.46
• DOI: 10.1016/j.ocecoaman.2021.106015
• Languages: English
• ISSNs: 0964-5691

Abstract

Abstract: Automatic identification system (AIS) refers to a new type of navigation aid system equipped in maritime vehicles to monitor ship performance. It provides trajectory information of vessels which can be used for the classification task. The classification results facilitate ocean surveillance and conservation, vessel management enhancement, fishery regulation, marine ecological sustainability, and nautical safety protection. Some progresses have been made based on machine learning or deep learning strategies to perform supervised learning by assuming that during the training process, the category labels of historical trajectory data are available. However, in reality, the label information may be difficult or expensive to obtain, resulting in that only a small part of the training data is labeled, and most of the training data is unlabeled. To address this issue, this paper proposes a semi-supervised deep learning approach to integrate the knowledge of unlabeled data for vessel trajectory classification. Here, we call our approach SSL-VTC. Specifically, we first extract vessel trajectories by integrating the kinematic and static information from historical AIS messages. Then, we design convolutional neural networks to extract feature representations from the vessel trajectories. Finally, we develop a semi-supervised learning algorithm based on the variational autoencoder to perform discriminative learning and generative learning simultaneously. In this way, our SSL-VTC framework can fully leverage the labeled data and unlabeled data during the training process. To the best of our knowledge, we are the first to utilize semi-supervised learning for vessel trajectory classification. Experimental results on the public AIS dataset show that our SSL-VTC can effectively extract feature representations from the AIS messages and its performance is significantly better than the traditional supervised learning methods. The approach and findings of this study provide important implications for researchers and stakeholders in the field of ocean management. Graphical abstract: Highlights: Propose a semi-supervised deep learning approach for vessel trajectory classification using AIS data. Utilize both kinematic and static information of AIS messages to extract vessel trajectories for the classification task. Conduct extensive experiments on the public AIS dataset to show the effectiveness of our approach. Provide implications of our study for ocean management.