Vessel sailing route extraction and analysis from satellite-based AIS data using density clustering and probability algorithms

The vessel Automatic Identification System (AIS) data collected by satellites have the features of large coverage area and large data volume, and they are instantaneous discrete data rather than time-continuous data, so the data has large dispersion with many noise points. This poses a challenge for vessel sailing route extraction. This paper proposes a vessel sailing route extraction method which consists of the fast Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Kernel Density Estimation-based Outlier Factor (KDE-based OF) noise reduction algorithm. The method in this paper firstly improves the clustering discrimination method in the DBSCAN algorithm to separate trajectories in different directions. Secondly, this paper extracts a fast clustering algorithm based on the density clustering algorithm to reduce its computing time overhead with satellite big data. Finally, this paper proposes the KDE-based OF processing algorithm, which calculates the outlier probability distribution value of the trajectory points through the algorithm to eliminate the edge trajectory points with low probability distribution. Based on the actual satellite vessel AIS data, this paper conducts multi-method comparisons and performance analysis experiments. Experiments show that the proposed method has the best stability and advancement.