VC-GAN: Classifying Vessel Types by Maritime Trajectories using Generative Adversarial Networks

As maritime transport is the backbone of global trade, it is important to ensure the safety and security for sea transportation effectively. However, the dependence on experienced human operators for maritime surveillance does not scale in terms of coverage. While the ship information and trajectory data from the Automatic Identification System (AIS) can be used to automate maritime surveillance, the AIS data may be modified deliberately or accidentally, resulting in difficulties in the detection of illegal maritime activities. We have developed VC-GAN for Vessel Classification using Generative Adversarial Networks to identify vessel types based solely on the vessel trajectories in areas of interest. Our VC-GAN framework adversarially trains a multi-class classifier by learning from the labelled AIS data to classify the types of the vessels of interest, as well as to detect the out-of-class vessels. We evaluated the proposed VC-GAN method on two maritime datasets in Europe and Southeast Asia. The experimental results showed that VC-GAN significantly outperformed other vessel classification methods, especially in detecting out-of-class vessels.