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, F 1 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.

中文翻译：

---

使用卷积神经网络基于自动识别系统 (AIS) 数据的船舶运动分类

摘要 随着 AIS 数据在海上运输中的广泛使用，越来越需要开发算法来有效地将船舶的 AIS 数据分类为不同的运动（静态、正常导航和操纵）。为了实现这一点，已经提出了一些使用标记特征的研究，但缺点是无法有效提取船舶运动信息的细节。此外，船舶在自由空间中运动，不同于公路车辆在道路网格中的运动，不方便直接将GPS数据分类方法迁移到AIS数据中。针对这些问题，本文提出了一种卷积神经网络-船舶运动模式分类（CNN-SMMC）算法。这种方法的底层概念是训练一个神经网络从标记的AIS数据中学习，利用这个训练好的网络可以有效地对未标记的AIS数据进行分类。更具体地说，首先设计了船舶运动图像生成和标记（SMIGL）算法，将船舶的 AIS 轨迹转换为不同的运动图像，以充分利用 CNN 的分类能力。然后，使用一系列功能层（卷积层、最大池化层、密集层等）构建了 CNN-SMMC 架构，用于船舶运动分类，并设计了七个实验来寻找 CNN-SMMC 的最佳参数。考虑到AIS数据的不平衡特征，选择了三个指标（平均准确率、F 1 分数和曲线下面积（AUC））来评估CNN-SMMC的性能。最后，选择了几种基准分类算法（K-最近邻（KNN）、支持向量机（SVM）和决策树（DT））与CNN-SMMC进行比较。结果表明，所提出的CNN-SMMC在AIS数据分类方面具有更好的性能。