SFR-Net: Scattering Feature Relation Network for Aircraft Detection in Complex SAR Images

Aircraft detection in synthetic aperture radar (SAR) images plays a significant role in dynamic monitoring and national security. Previous methods have difficulty in obtaining the desirable detection performance due to the interference of complex scenes and diversity of aircraft sizes. In order to solve these problems, we propose an innovative scattering feature relation network (SFR-Net) in this paper. First, considering that the strong scattering points of the aircraft in SAR images are usually discrete, we leverage the proposed scattering point relation module to fulfill the analysis and correlation of scattering points. By enhancing the characteristics and relationships among the scattering points, this method is beneficial to guarantee the completeness of aircraft detection results. Second, we design a salient fusion module to adaptively aggregate the features from different layers of SFR-Net with rich semantic information and plentiful details, which can highlight the significant objects with different sizes and enhance the distinguishable features. Third, to reduce the false alarm and improve the localization accuracy, the contextual feature attention is presented to capture the global spatial and semantic information with a large receptive field. Overall, the SFR-Net is designed based on the SAR imaging mechanism and the scattering characteristics of aircrafts. The extensive experiments are conducted on the SAR aircraft detection dataset (AIRD) from the Gaofen-3 satellite to demonstrate the effectiveness of the SFR-Net and also illustrate that our method achieves the state-of-the-art performance.