Decorrelated scenarios, such as vegetated areas, are influenced by multiple scattering mechanisms and temporal decorrelation, which introduc
Decorrelated scenarios, such as vegetated areas, are influenced by multiple scattering mechanisms and temporal decorrelation, which introduce phase noise and coherence estimation bias, posing challenges for phase linking (PL). The eigenvalue decomposition (EVD) method, well-suited for multiscattering environments, mitigates these issues by decomposing signals into orthogonal eigenvectors, thus reducing the impact of multiscattering mix on PL. However, EVD's effectiveness relies on the assumption of polarimetric stationarity, which is often violated in low-coherence scenarios due to the dynamic nature of vegetation and meteorological factors. Multipolarization SAR data can address this challenge by enabling quantitative assessment of polarimetric stationarity via likelihood statistics of time series polarimetric covariance matrices. To enhance EVD, we introduce the time series polarimetric scattering consistency contribution (TSCC) metric, which evaluates the contribution of each interferometric pair to overall scattering consistency. The TSCC metric, based on the ratio of local to global scattering consistency, identifies interferometric data that meet the polarimetric stationarity assumption. Based on the scattering amplitude variations, it offers an available data quality assessment in decorrelated regions. Replacing traditional coherence weights, the TSCC metric modifies EVD to prioritize temporally stable interferometric pairs, improving phase consistency with actual deformation signals. Experimental results show that the proposed method outperforms traditional methods, achieving a 15% improvement in point density for distributed scatterers in evergreen forest areas and an 82% improvement in point number of positive posterior coherence difference compared to classical EVD.
