Langevin Sampling Plug-and-Play Synthetic Aperture Radar Imaging Algorithm

Abstract

Synthetic aperture radar (SAR) is a widely used active imaging system for remote sensing applications. However, traditional signal processing-based SAR imaging algorithms suffer from coherent speckle problems. Recently, statistical SAR imaging methods such as the FESAR model and plug-and-play (PnP) SAR imaging methods have been applied to suppress the speckle phenomenon. However, they are sometimes unstable and require an elaborate hyperparameter adjustment strategy during the iteration process. We propose extending PnP statistical imaging with Langevin sampling, called the Langevin-PnP algorithm. To construct the Langevin-PnP algorithm, we provide an in-depth analysis of the PnP framework and incorporate Langevin dynamics into its iteration trajectory. We also present a convergence guarantee for Langevin-PnP because the injected stochasticity affects the convergence condition under the law of probability. The experimental results showed that our proposed Langevin-PnP maintained the best performance over other statistical imaging methods, both in the simulated experiments and the RadarSat-SAR data experiments.