Scattering Features Spatial-Structural Association Network for Aircraft Recognition in SAR Images

Abstract

Due to the all-day, all-weather imaging characteristics of Synthetic Aperture Radar (SAR), aircraft recognition in SAR images is emerging as an vital issue. Electromagnetic scattering (ES) characteristics of target are the unique features of SAR systems. In particular, it is more pronounced for aircraft targets with discrete appearance. However, the existing deep learning methods effectively extract features in image domain, ignoring the potential ES features. To obtain more valuable features for SAR aircraft recognition, an innovative scattering features spatial-structural association network (SFSA) is proposed in this paper. In this work, the strong scattering points (SSPs) of aircraft are extracted and converted into graph structure data, which more directly represent the spatial-structural association among SSPs and clearly reflect the geometric structure of the aircraft target. Subsequently, the graph convolutional neural network (GCN) is employed to extract the structural and high-level semantic ES features. Then, a modified VGGNet is designed to effectively extract the image domain features of aircraft with extremely discrete appearances. In brief, the SFSA network is an end-to-end network that integrates structural ES features and more discriminative image domain features of the aircraft to achieve higher recognition performance. It is also demonstrated that structural ES features facilitate the recognition of aircraft. Experiments on the SAR aircraft dataset validate the effectiveness of SFSA network compared to traditional CNN-based SAR recognition networks and graph neural networks (GNN).