# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 1 Introduction  
The analysis and mining of ship trajectory data hold significant application value in various fields, such as maritime traffic safety [1], traffic management and planning [2, 3], and marine environmental protection [4]. This has spurred extensive research on ship trajectories, including trajectory prediction [5, 6, 7, 8], anomaly detection [9, 10, 11], channel planning [12, 13, 14], behavior pattern recognition [15, 16, 17, 18, 19], and traffic flow analysis [20]. Trajectory category information plays a crucial supporting role in anomaly behavior detection and ship navigation supervision. However, the actual collected ship trajectory data may suffer from quality issues such as missing category information and labeling errors, which hinder the accuracy and reliability of subsequent tasks like navigation supervision and anomaly behavior detection. Therefore, research on ship trajectory classification methods is of great theoretical and practical significance. Current ship trajectory classification algorithms mainly include traditional machine learning-based classification algorithms and neural network-based classification algorithms. The former includes algorithms based on logistic regression [21], conditional random fields [22], decision trees [23], random forests [24, 25], K-nearest neighbors (KNNs) [26],support vector machines [27], etc. Although these algorithms have achieved some success, their reliance on expert-defined features makes the feature representation susceptible to subjective influences. Additionally, differences in navigation rules and environments across different sea areas necessitate frequent adjustments to feature combinations and parameter settings, limiting their practical application effectiveness. In contrast, neural network-based methods automatically learn deep-level features through nonlinear feature transformations, avoiding the limitations of manual feature engineering, and exhibit better generalization in practical applications due to their end-to-end learning capabilities. Currently, neural network-based ship trajectory classification algorithms mainly include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and their variants, leveraging their translation invariance and convolutional kernel weight-sharing properties, can achieve feature extraction from the local to the global with a relatively small number of parameters, forming high-level abstract feature representations. This characteristic has facilitated their application in the field of trajectory classification. [PERSON] et al. [28] transforms the ship trajectory classification problem into an image recognition task by visualizing four types of ship trajectory motion patterns and introducing pre-trained CNN models such as the Visual Geometry Group Network (VGG) [29] for classification. [PERSON] et al. [30] generated trajectory images containing navigation pattern information based on the motion and shape characteristics of ship trajectories and constructed a CNN to achieve trajectory classification. [PERSON] et al. [31] converted ship trajectory data into images and built a deep convolutional neural network classification model based on ResNet50. RNNs and their variants, as neural network models specifically designed for processing sequential data, can effectively capture the temporal dependencies of sequential data, making them widely used in ship trajectory data analysis. [PERSON] et al. [32] proposed a partitioned gated recurrent unit (pGRU) architecture, enhancing the model's expressive power in low-dimensional and heterogeneous feature spaces through partitioned activation learning in the feature space. [PERSON] et al. [33] innovatively combined time series models with computer vision models, using bidirectional long short-term memory networks (Bi-LSTM) to extract temporal features from AIS data streams. To further enhance the completeness and richness of ship trajectory feature representations, researchers have begun exploring hybrid architectures combining CNNs and RNNs.