Robust Refinement Method for Coherence Matrix in Time-Series InSAR Phase Linking

GUAN Jianjun; TANG Xinming; LI Song

doi:10.13203/j.whugis20250153

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Robust Refinement Method for Coherence Matrix in Time-Series InSAR Phase Linking

更新时间：2026-11-05

- Robust Refinement Method for Coherence Matrix in Time-Series InSAR Phase Linking
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 11, Pages: 2198-2212(2025)
- 作者机构：
  
  1.武汉大学电子信息学院，湖北武汉，430079
  2.自然资源部国土卫星遥感应用中心，北京，100048
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250153
  CLC： P237
- Received：29 May 2025，
  
  Published：05 November 2025
- 稿件说明：
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管建军,唐新明,李松.面向时序InSAR相位链接的鲁棒相干矩阵精化方法[J].武汉大学学报（信息科学版）,2025,50(11):2198-2212.

GUAN Jianjun,TANG Xinming,LI Song.Robust Refinement Method for Coherence Matrix in Time-Series InSAR Phase Linking[J].Geomatics and Information Science of Wuhan University,2025,50(11):2198-2212.
管建军,唐新明,李松.面向时序InSAR相位链接的鲁棒相干矩阵精化方法[J].武汉大学学报（信息科学版）,2025,50(11):2198-2212. DOI：10.13203/j.whugis20250153

GUAN Jianjun,TANG Xinming,LI Song.Robust Refinement Method for Coherence Matrix in Time-Series InSAR Phase Linking[J].Geomatics and Information Science of Wuhan University,2025,50(11):2198-2212. DOI：10.13203/j.whugis20250153

摘要

从时间序列堆栈数据中准确恢复像元时序一致性相位即相位链接(phase linking，PL)，是联合永久散射体和分布式散射体的混合时序InSAR（interferometric synthetic aperture radar，InSAR）分析中的关键处理步骤，基于最大似然估计的PL技术受样本相干矩阵（sample coherence matrix，SCM）估计偏差的制约，在求逆时偏差会被显著放大，从而错误地强调不可靠的相位观测。针对散射向量的非高斯性干扰和时间失相干效应可能显著引起SCM偏差的问题，根据样本散射向量自身特点及初始SCM内部结构特性，实现复信号统计特性与物理相干机制的双向耦合，提出了一种自适应时间失相干锥化收缩估计方法，以准确估计SCM，并与基于特征分解的干涉相位最大似然估计（eigendecomposition-based maximum-likelihood-estimator of interferometric phase，EMI）相结合，精确恢复时序一致性相位。模拟与真实场景实验结果表明，在失相干严重、低信噪比的复杂地表环境中，该估计器相较其他方法，在有效改善相干矩阵估计偏差的同时，显著提高了时序一致性相位的估计精度。

Abstract

Objectives

Accurately retrieving time-series consistent phase information from time-series stacked data through phase linking (PL) serves as the critical processing step in hybrid time-series interferometric synthetic aperture radar analysis that jointly utilizes permanent scatterers and distributed scatterers. PL techniques based on maximum likelihood estimation are constrained by the estimation bias of the sample coherence matrix (SCM). During matrix inversion

this bias can be significantly amplified

potentially leading to the overemphasis of unreliable phase observations.

Methods

To address the issue that non-Gaussian scattering vector perturbations and temporal decorrelation can introduce significant bias into the sample coherence matrix (SCM)

this paper proposes the temporal decoherence taper shrinkage for SCM (TDTS-SCM) method. By leveraging both the statistical characteristics of the scattering vectors and the internal structure features of the initial SCM

the method achieves a bidirectional coupling between statistical properties of complex signals and underlying physical coherence mechanisms. Integrated with the eigendecomposition-based maximum likelihood estimator

the proposed approach enables accurate estimation of the SCM and robust recovery of time-seires consistent interferometric phase.

Results

Both simulated and real-world experimental results demonstrate that

in complex surface environments characterized by severe decorrelation and low signal-to-noise ratios

the proposed estimator significantly outperforms existing methods. It not only effectively mitigates the estimation bias of the coherence matrix， but also substantially improves the accuracy of time-series consistent phase retrieval.

Conclusions

The proposed method effectively reconstructs dense deformation phase fringes

increases the number of identifiable measurement points

and enhances the effectiveness and accuracy of subsequent phase unwrapping. As a result

it reduces monitoring blind spots and improves the detection capability of deformation in low-coherence environments.

关键词

Keywords

references

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