Resumen
With the aim of solving the problems of ship trajectory classification and channel identification, a ship trajectory classification method based on deep a convolutional neural network is proposed. First, the ship trajectory data are preprocessed using the improved QuickBundle clustering algorithm. Then, data are converted into ship trajectory image data, a dataset is established, a deep convolutional neural network-based ship trajectory classification model is constructed, and the manually annotated dataset is used for training. The fully connected neural network model and SVM model with latitude and longitude data as input are selected for comparative analysis. The results show that the ship trajectory classification model based on a deep convolutional neural network can effectively distinguish ship trajectories in different waterways, and the proposed method is an effective ship trajectory classification method.