MGSFA-Net: Multiscale Global Scattering Feature Association Network for SAR Ship Target Recognition

Abstract

Deep learning has offered new ideas in SAR ship target recognition. Although many methods improve the recognition performance through the improvement of loss function and migration of deep networks, scattering features as the important intrinsic features of SAR targets, need to be considered in the SAR ship recognition tasks. To introduce the scattering features into the deep network and characterize the features of ship targets more comprehensively, a multiscale global scattering feature association network (MGSFA-Net) for SAR ship target recognition is proposed in this article. In the network, the SAR ship target is first separated from the background by fine target segmentation. Then, the scattering centers (SCs) of ship targets are extracted and converted to local graph structures based on the $k$-nearest neighbors algorithm. The local graph structures are associated by the scattering center feature association module and enhanced by the multiscale feature enhancement module to produce the multiscale global scattering features. Moreover, the deep features of the targets are extracted by the multikernel deep feature extraction module to characterize the high-dimensional information. Finally, the scattering features and deep features are fused by weighted integration to enrich the diversity of features. The experimental results on the FUSAR-Ship and OpenSARShip dataset show that the MGSFA-Net can significantly improve the recognition performance, even on a few-shot condition with the accuracy increasing over 2%–3%. The feature distribution and visualization show the effectiveness of the MGSFA-Net to characterize the multiscale global scattering association features.

Keywords

• Deep learning
• multiscale
• scattering feature association
• ship recognition
• synthetic aperture radar (SAR)