Research on AIS Data Aided Ship Classification in Spaceborne SAR Images

Abstract

The continuous development of spaceborne synthetic aperture radar (SAR) technology promotes the research of ship classification and plays an important role in maritime surveillance. At present, the mainstream ship classification based on the deep learning method in SAR images has achieved a state-of-the-art performance, but it heavily depends on plenty of labeled samples. Compared with SAR images, the automatic identification system (AIS) can provide a large amount of data that is relatively easy to obtain and contains rich ship information. Therefore, in order to solve the problem of ship classification in SAR images with limited samples, a ship object classification method by AIS data aided is proposed in this paper. Specifically, we first train the ship classification model SMOTEBoost on AIS data, and then transfer the trained model to SAR images for ship type prediction. Experimental results show that the proposed method achieves classification accuracy as high as 93%, which proves that AIS data transfer can effectively solve the problem of ship classification in SAR images with limited samples.