Recognition of Deformation Military Targets in the Complex Scenes via MiniSAR Submeter Images With FASAR-Net

Ground-armored weapons have a high detection value in military operations. Satellite synthetic aperture radar (SAR) cannot accurately detect military targets with meter-level sizes limited by the resolution of sensors. Airborne SAR has strict experimental conditions and cannot be applied in actual battlefield environments. Miniature SAR (MiniSAR) sensors, which combine the advantages of submeter-level ultrahigh resolutions and flexible flight, play a crucial role in recognizing military targets. In this article, various small military targets in real complex ground scenarios are detected with the MiniSAR of NUAA. However, there are still two difficulties. First, because of a limitation in the number of flight circles, the number of obtainable military target samples is not sufficient to adapt to the traditional deep learning methods that rely on a large number of image samples. Second, due to the imaging systems and different depression angles of MiniSAR, the SAR images of MiniSAR suffer from the same deformation challenge as the moving and stationary target acquisition and recognition (Mstar) with high depression angle. To address these two challenges, we propose a few-shot learning and adversarial domain learning SAR Net (FASAR-Net) framework based on few-shot learning with meta learning and adversarial domain learning, combined with the inherent scattering features of the SAR targets. Furthermore, we validate the reliability and accuracy of this algorithm on Mstar and our datasets, and the result of recognizing small SAR targets is compared with our algorithm and other classical algorithms. We conclude that the proposed algorithm has high accuracy in the recognition of the deformation of small targets under the few sample condition.