Research on fishing vessel recognition based on vessel behavior characteristics from AIS data

作者：Xiao Han, Yang Zhou, Jianjun Weng, LONG CHEN, Kang Liu · 发表于：Frontiers in Marine Science · 年份：2025 · DOI：10.3389/fmars.2025.1547658 · 被引用次数：9 · 研究领域：Maritime Navigation and Safety

The Automatic Identification System (AIS) is one of the most important navigation assistance systems and plays a pivotal role in vessel monitoring. However, some fishing vessels disguise themselves as other vessel types during fishing bans to engage in illegal fishing activities, causing significant damage to marine ecosystem. To address this challenge and accurately identify vessel types, a BP-AdaBoost classification algorithm is developed by integrating backpropagation (BP) neural networks with ensemble learning techniques. The proposed algorithm leverages the AdaBoost method to combine multiple BP neural network weak classifiers into a strong classifier, effectively mitigating the slow convergence rate and susceptibility to local optima inherent in BP neural networks. By configuring the output nodes of the BP neural network to match the number of target classes, the AdaBoost algorithm achieves robust multi-class classification functionality. Historical AIS data are analyzed to extract static features, vessel behavior features, and temporal features for vessel classification. To minimize model overfitting, the Maximal Information Coefficient algorithm is employed to assess feature importance, and optimal feature combinations are determined through systematic feature selection experiments. Experiments are conducted using AIS data from the Pearl River Estuary in China, targeting the classification of cargo ships, fishing vessel, tanker, and passenger ships. The performance of...