In multitemporal InSAR, phase linking (PL) refers to the estimation of a single-reference interferometric phase history for distributed scatterers (DS) from the information contained in the sample coherence matrix. Because the phase information in this matrix is typically inconsistent, DS processing needs practical reliability indicators to decide whether a pixel's PL estimate is sufficiently supported by the data for subsequent deformation analysis. For maximum-likelihood estimation, uncertainty can be quantified via Fisher-information-based covariance estimates, but no analogous, generally applicable uncertainty quantification is available for the broad range of non-ML methods. We propose three heuristic quality coefficients within a unified mathematical framework that covers common PL methods: (1) a method-specific goodness-of-fit coefficient that normalizes the achieved PL objective between a method-consistent upper bound and an empirically modeled noise floor level; (2) a closure phase coefficient computed from the sample coherence matrix in advance; and (3) an ambiguity coefficient that compares the obtained PL estimate with the best alternative in its orthogonal complement in the solution space. All coefficients are normalized to the interval $[0,1]$, where 1 indicates maximum reliability and 0 matches the behavior expected under pure noise. Simulations under exponential and seasonal decorrelation models show that the goodness-of-fit coefficient tracks the normalized absolute phase error most consistently, whereas the closure phase coefficient provides an a priori indicator for pre-screening. Experiments on a TerraSAR-X stack over Visp, Switzerland, reveal plausible spatial patterns across urban and vegetated areas and show that the ambiguity coefficient provides complementary information, especially in regions with temporally varying scattering mechanisms.

翻译：在多时相InSAR中，相位连接是指从样本相干矩阵包含的信息中，为分布式散射体估计单参考干涉相位历史的过程。由于该矩阵中的相位信息通常不一致，DS处理需要实用的可靠性指标，以判断像素的相位连接估计是否得到数据的充分支持，从而用于后续形变分析。对于最大似然估计，不确定性可通过基于Fisher信息量的协方差估计来量化，但对于广泛使用的非ML方法，尚无类似且普遍适用的不确定性量化方法。我们提出三种启发式质量系数，统一数学框架涵盖常见相位连接方法：(1) 方法特定的拟合优度系数，将所实现的相位连接目标归一化于方法一致的上界与经验建模的噪声基底之间；(2) 闭合相位系数，由样本相干矩阵预先计算得出；(3) 模糊度系数，将所得相位连接估计与解空间中正交补的最佳替代值进行比较。所有系数均归一化至$[0,1]$区间，其中1表示最大可靠性，0对应纯噪声下的预期行为。在指数与季节性去相关模型下的仿真表明，拟合优度系数最一致地追踪归一化绝对相位误差，而闭合相位系数则为预筛选提供先验指标。在瑞士菲斯普地区的TerraSAR-X数据堆栈上的实验揭示了城市与植被区域中合理的空间模式，并显示模糊度系数尤其在散射机制随时间变化区域提供互补信息。