Identifying Key Features in Aggregated Real-Life AIS-Messages for Classification of Maritime Vessels by Random Forests

Classification of vessels is a major task in many duties of maritime surveillance. In previous work, ad hoc approaches for classifying maritime vessels were applied to large sets of single AIS messages. The resulting performance has been explored based on real-life datasets in different maritime areas. The approaches were used in a particular application scenario of detecting AIS spoofing in fishery scenarios. Seeing good classification results, in particular by Random Forests, the questions arise whether these at least partly resulted from overfitting phenomena and which of the feature of classification in this problem are most relevant. To address these questions in the current contribution, AIS messages are aggregated per vessel and by the hour by statistical means providing a new AIS feature representation as a basis for classification. By providing a new data representation to the Random Forest classifier, we evaluate classification performance. Then we restrict the presented data to the semantic feature subgroups of the vessel’s positional, course, speed, and dimensions data individually and identify the key features of the classification. The results show that in our exemplary maritime experiments, classification relies mostly on vessels’ positional and dimensional information.