A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification

To improve the feature extraction method for ship trajectories and enhance trajectory classification performance, this paper proposes a ship trajectory classification model that combines a one-dimensional residual network (ResNet1D) and an attention-based Long short-term memory network (AttLSTM). The model aims to address the limitations of traditional methods in extracting feature patterns jointly represented by non-adjacent local regions in ship trajectories, optimized through the introduction of a self-attention mechanism. Specifically, the model first utilizes the ResNet1D module to progressively extract implicit motion pattern features from local to global levels, while the AttLSTM module captures temporal sequence features of ship trajectories. Finally, the fusion of these two types of features generates a more comprehensive and rich spatiotemporal motion feature representation, enabling accurate classification of five types of ship trajectories, including towing vessels, fishing vessels, sailing vessels, passenger ships, and tankers. Experimental results show that this model excels on extensive real-world trajectory datasets, achieving a classification accuracy of 89.7%, significantly outperforming models relying solely on single feature sets or lacking integrated attention mechanisms. This not only validates the model’s superior performance in ship trajectory classification tasks but also demonstrates its potential and effectiveness for practical applications.