Deformable Scattering Feature Correlation Network for Aircraft Detection in SAR Images.

Aircraft detection is a valuable but challenging task in synthetic aperture radar (SAR) automatic target recognition (ATR). Because of the complicated electromagnetic imaging mechanism of SAR, the SAR image of aircraft appears as a distributed collection of discrete scattering points that varies significantly with imaging conditions, like different incident angles, bringing great challenges to existing convolution neural network (CNN)-based detection methods for accurate aircraft detection. To address these challenges, we analyze and leverage the scattering characteristics of multiscale SAR aircraft to propose a novel SAR aircraft detector named deformable scattering feature correlation network (DSFCN). First, to deal with the discreteness of SAR aircraft, we propose a new Transformer-based backbone named scalable Swin Transformer backbone (SSTB) to replace a conventional CNN-based one, to effectively extract hierarchical scattering features from multiscale aircraft. Second, to cope with the varying image appearance of SAR aircraft, we design a deformable region correlation module (DRCM) to flexibly correlate strong scattering regions (SSRs) that carry aircraft salient features. Various interpretable experiments conducted on a real-measured Gaofen-3 SAR aircraft dataset demonstrate the superiority and reliability of our DSFCN over other representative CNN-based methods.