Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data.

中文翻译：

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MTInSAR中相位链接和模糊度检测的相干矩阵偏差的抑制

相位去相关作为主要误差源之一，限制了干涉合成孔径雷达 (InSAR) 在低相干区域上进行形变映射的能力。虽然已经实现了几种方法来减少去相关噪声，例如，通过相位链接和空间和时间滤波器，但当存在相干估计偏差时，它们的性能会恶化。我们提出了一种基于弧的方法，该方法允许基于时空域中的迭代加权最小二乘法 (WLS) 重建展开的间隔相位时间序列。该方法的主要特点是相位优化和展开可以通过空间和时间迭代WLS联合进行，相干矩阵偏差对估计的影响可以忽略不计。此外，线性结构使得实现适用于干涉图的小子集，为未来的大 SAR 数据提供了有效的解决方案。我们使用具有不同去相关机制的模拟和真实数据证明了所提出方法的有效性，并将我们的方法与最先进的相位重建方法进行了比较。就减少模拟数据中的均方根误差 (RMSE) 和增加实际数据中相干测量的密度而言，可以实现实质性改进。