We propose an atmospheric turbulence statistical characteristic-enhanced stacking-interferometry synthetic aperture radar (InSAR) method to more accurately derive the average deformation velocity for slow or linear surface displacements. It can be used to estimate the variance and covariance matrix (VCM) for time-series InSAR observations pixel by pixel. Moreover, a minimum variance linear estimator is used to calculate optimal weights for interferogram stacking. Compared with traditional stacking, our method is an improvement, and its advantages include the following: 1) the relevance between time-series atmospheric delays of interferograms is considered; 2) the atmospheric turbulence is better suppressed; and 3) the performance is not limited by the quantity of interferograms. The effectiveness of the new method is verified through a series of simulated experiments and real Sentinel-1 experiments over Southern California. The results demonstrate that our proposed method is more effective and robust in removing turbulent atmospheres.

中文翻译：

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考虑大气湍流统计特征的细化Stacking-InSAR


我们提出了一种大气湍流统计特征增强叠加干涉合成孔径雷达（InSAR）方法，以更准确地得出缓慢或线性表面位移的平均变形速度。它可用于逐像素估计时间序列 InSAR 观测的方差和协方差矩阵 (VCM)。此外，最小方差线性估计器用于计算干涉图叠加的最佳权重。与传统的叠加方法相比，我们的方法是一种改进，其优点包括：1）考虑了干涉图的时间序列大气延迟之间的相关性； 2）大气湍流得到更好的抑制； 3）性能不受干涉图数量的限制。通过一系列模拟实验和南加州Sentinel-1的真实实验验证了新方法的有效性。结果表明，我们提出的方法在消除湍流气氛方面更加有效和鲁棒。