A Lightweight Network for Radio Frequency Interference Suppression in SAR Amplitude Images Using Matrix Representation and Decomposition

Radio frequency interference (RFI) is an important factor affecting microwave remote sensing observations, causing random degradation of synthetic aperture radar (SAR) images. Due to the huge amount of raw echo and single-look complex (SLC) data, there are some SAR interpretation scenarios when only amplitude images can be obtained, and traditional signal transformation and matrix operations can hardly meet the suppression requirements in the absence of phase information at this time. Although deep learning algorithms have made some progress on this issue, they still suffer from the following limitations: 1) there are few models dedicated to SAR RFI in the image domain and 2) the network structure is relatively complex due to not fully exploiting the physical characteristics of SAR and RFI. To this end, we propose an end-to-end suppression network (PMNet), which includes a novel explainable feature decomposition module (FDM) based on the idea of nonnegative matrix factorization and a composite loss function to achieve dynamic separation of foreground and background features of supervised RFI-contaminated images. The ablation experiment proves that compared with the baseline algorithm, the visual similarity of the proposed PMNet on the test set can be improved by up to 14.15%. The suppression result on Sentinel-1 and Gaofen-3 real data also verifies the effectiveness of the PMNet in different SAR platforms.