Abstract
A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety, security and sustainability. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly challenging in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture which disrupts surveillance operations and negatively affects research activities that use AIS as a source of ground truth. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data.
A series of Monte Carlo tests are performed to evaluate the performance and robustness of the classification-aided data association technique. Subsequently, two case studies of dense shipping environments in the UK are used to validate a multisensor fusion framework based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic areas, and the confidence in the data association is improved when using class (i.e. ship type) information. Additionally, the effectiveness of co-located SAR and AIS datasets collected from the UK’s first spaceborne radar mission, NovaSAR-1, is evaluated. Finally, future research directions involving the fusion of SAR and AIS data are discussed.