Synthetic aperture radar （SAR） 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. To address the above problem， numerous algorithms have been proposed. Although inpainting networks have achieved excellent results， their generalization is unclear. Whether they still work effectively in cross-sensor experiments needs further verification. Through time-frequency analysis to interference signals， this work finds that interference holds domain invariant features between different sensors. Therefore， this work reconstructs the loss function and extracts the domain invariant features to improve its generalization. Ultimately， this work 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 experiments， our method can still achieve excellent results. In cross-sensor experiments， training data and testing data come from different radar platforms with different parameters， so cross-sensor experiments can provide evidence for the generalization.