STC-Net: Scattering Topology Cue-based Network for Aircraft Detection in SAR Images

Aircraft detection in synthetic aperture radar (SAR) imagery is significant due to its critical role in various applications, including surveillance, reconnaissance, and security. However, given the background interference and discreteness of aircraft scattering, detectors are prone to acquire unremarkable aircraft features. These factors lead to false alarms and present difficulties in locating aircraft accurately. This article proposes an innovative scattering topology cue-based network (STC-Net), which enhances aircraft discriminability and more accurately evaluates the quality of the prediction results. We model the aircraft with the star topology (ST), which not only emphasizes critical components like the nose and wings but also explicitly links them as a cohesive unit. Based on the cue of ST, the star topology space fusion module (ST-SFM) and the star topology channel attention module (ST-CAM) are designed. The former integrates discrete components to reestablish the aircraft features based on neighboring information of ST, while the latter suppresses background interference to highlight the aircraft by exploiting node information of ST. In addition, completeness and consistency loss (CCLoss) function that includes the completeness-aware label and the positive sample weighting function is introduced. The completeness-aware label describe the localization accuracy by incorporating the degree of overlap of predicted results on ST, while the positive sample weighting function enhances the consistency of the classification and localization branches. Furthermore, experiments conducted on the Gaofen-3 SAR Aircraft Detection Dataset (GF3ADD) and the publicly available SAR-AIRcraft-1.0 dataset demonstrate the effectiveness and generalizability of STC-Net, with our method achieving state-of-the-art performance.