Research on trajectory hierarchical classification based on deep learning networks

In response to the issue that existing trajectory classification methods fail to fully consider the time series features and spatial structure features of trajectories, leading to a decline in classification accuracy, this paper proposes a trajectory hierarchical classification method based on deep learning networks. First, ship trajectories are transformed into image layers, and a trajectory image layer classification model based on the Swin-Transformer network is constructed. Next, for the trajectory sequence layer, a multi-dimensional information-based trajectory compression algorithm is utilized to optimize the input of trajectory sequences, and a trajectory sequence layer classification model based on the Gained-Transformer-Network deep learning network is developed. At last, a confidence-based fusion layer trajectory classification model is established to improve the accuracy of layered trajectory classification. Experimental validation shows an average classification accuracy of 90%, with the performance of the ensemble classifier improving by an average of 11% compared to other single classifiers, and an average F1 score of 0.82. The results indicate that the newly proposed method and the new ensemble classifier demonstrate good classification effectiveness for ship trajectories.