UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

Chen, X; Liu, Y; Achuthan, K; Zhang, X; (2020) A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network. Ocean Engineering , 218 , Article 108182. 10.1016/j.oceaneng.2020.108182.

[img] Text
A_ship_movement_classification_based_on__AIS__using_CNN.pdf - Accepted version
Access restricted to UCL open access staff until 7 October 2021.

Download (4MB)

Abstract

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship’s AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labelled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle’s movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labelled AIS data, and the unlabelled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship’s AIS trajectories into different movement images to make a full use of the CNN’s classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data.

Type: Article
Title: A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network
DOI: 10.1016/j.oceaneng.2020.108182
Publisher version: https://www.sciencedirect.com/journal/ocean-engine...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10111124
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item