Classifying Ships in SAR Images by Using Contour Bias Features and Transfer Learning

With the development of synthetic-aperture radar (SAR) image interpretation technology in recent years, classifying detected ships based on SAR images has become an important trend in ocean monitoring. However, owing to the underlying imaging mechanism, the texture of SAR images typically contains noise that cannot be easily eliminated. Therefore, in this study, we consider more stable target contour features to classify SAR images of marine vessels. Based on a deep learning algorithm, we propose a method to obtain contour bias features by employing style transfer learning to classify detected ships from SAR data. We also adopt transfer learning to improve the uneven distribution of SAR datasets of representative ships. The results of experiments conducted to evaluate the proposed approach show that contour bias features improve the generalization performance and classification accuracy of the model. They also show that transfer learning effectively avoids the problem of data imbalance and, thus, improves classification accuracy on the OpenSARShip 2.0 database.