A Principal Component Analysis Perspective for RFI Mitigation in Synthetic Aperture Interferometric Radiometer

In the field of Earth remote sensing, synthetic aperture interferometric radiometers (SAIR) are employed to retrieve geophysical parameters, such as soil moisture and ocean salinity, through inverse brightness temperature (BT) images. However, the high sensitivity of SAIR makes it susceptible to interference from radio frequency interference (RFI) sources, which significantly degrade the quality of BT images by contaminating the visibility function. To address this issue, this article proposes a robust imaging framework based on principal component analysis to mitigate RFI and recover scene BT. First, using a covariance matrix model, we analyze the low-rank property of RFI sources and the sparsity of natural scenes through theoretical analysis and experiments. Subsequently, leveraging these low-rank and sparse properties, we propose a reweighted nuclear norm (RNN) model and a reweighted matrix factorization (RMF) model to suppress RFI sources while recovering the visibility functions of natural scenes. These two RFI mitigation models are then solved using the alternating direction method of multipliers (ADMM). Finally, the discrete Fourier transform (DFT) method is applied to obtain BT images from the recovered visibility functions. Results from both simulated and measured data confirm the efficacy of the proposed method.