DIFNet: SAR RFI suppression based on domain invariant features

Synthetic aperture radar is a high-resolution two-dimensional imaging radar, however, during the imaging process, SAR is susceptible to intentional and unintentional interference, with radio frequency interference (RFI) being the most common type, leading to a severe degradation in image quality. Although inpainting networks have achieved excellent results, their generalization is unclear, and whether they still work effectively in cross-sensor experiments needs further verification. Through time-frequency analysis of interference signals, we find that interference holds domain invariant features between different sensors. Therefore, this paper reconstructs the loss function and extracts the domain invariant features to improve the generalization. Ultimately, this paper proposes a SAR RFI suppression method based on domain invariant features, and embeds the RFI suppression into SAR imaging process. Compared to traditional notch filtering methods, the proposed approach not only removes interference but also effectively preserves strong scattering targets. Compared to PISNet, our method can extract domain invariant features and holds better generalization ability, and even in the cross-sensor experiment, our method can still achieve excellent results.