Abstract
A novel SAR-AIS data association technique is proposed consistent with being used in dense shipping environments, where association of SAR and MS datasets is non-trivial and SAR false alarm rates are typically high. A ship classification model based on transfer learning classifies ship types in SAR imagery. The classification results are subsequently used in the SAR-AIS data association, which uses a rank-ordered assignment technique. The methodology is validated using a Sentinel-1 SAR product and terrestrial-based MS product acquired from the Gulf Coast, USA. Results show optimal data association which is improved using class (i.e. ship type) information.