MASFF-Net: Multiazimuth Scattering Feature Fusion Network for SAR Target Recognition

Deep learning methods have made significant progress in the field of synthetic aperture radar (SAR) target recognition, effectively extracting target image domain features. However, they generally overlook the inherent electromagnetic scattering (ES) characteristics of targets, which limit recognition accuracy and physical interpretability. To address this issue, this article proposes a multiazimuth scattering feature (MASF) fusion network that integrates ES features with image domain features for SAR target recognition. Initially, we design an MASF extraction module to acquire SAR subaperture image data from multiple azimuths and capture the strong scattering point features of multiazimuth SAR images, whose spatial distribution characterizes the geometric structure information of the target. Subsequently, we propose a hierarchical multiscale feature extraction module to explore the global semantic and local detail features of the target. Finally, we design a scattering-guided feature fusion module, which achieves the organic fusion of ES features and image features under the guidance of geometric scattering prior information, and enhances key region features using a multibranch coordinate attention mechanism. Experiments on a self-constructed measured dataset of ten types of SAR vehicle targets, the moving and stationary target acquisition and recognition benchmark dataset, and the SAR-AIRcraft-1.0 aircraft dataset demonstrate that this method achieves higher recognition performance while maintaining physical interpretability.