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.

中文翻译：

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Langevin 采样即插即用合成孔径雷达成像算法


合成孔径雷达（SAR）是一种广泛用于遥感应用的主动成像系统。然而，传统的基于信号处理的SAR成像算法存在相干散斑问题。近年来，FESAR模型和即插即用（PnP）SAR成像方法等统计SAR成像方法已被应用于抑制散斑现象。然而，它们有时不稳定，需要在迭代过程中精心设计超参数调整策略。我们建议使用 Langevin 采样扩展 PnP 统计成像，称为 Langevin-PnP 算法。为了构建 Langevin-PnP 算法，我们对 PnP 框架进行了深入分析，并将 Langevin 动力学纳入其迭代轨迹中。我们还为 Langevin-PnP 提供了收敛保证，因为注入的随机性会影响概率定律下的收敛条件。实验结果表明，我们提出的 Langevin-PnP 在模拟实验和 RadarSat-SAR 数据实验中都保持了优于其他统计成像方法的最佳性能。