A Joint M-Estimation and Bayesian Estimation Method for DS-InSAR Deformation Solution

LI Xiaotian; XIE Lei; JIANG Kun; SHAN Baojun; ZHU Lingjie; XU Wenbin

doi:10.13203/j.whugis20240125

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A Joint M-Estimation and Bayesian Estimation Method for DS-InSAR Deformation Solution

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- A Joint M-Estimation and Bayesian Estimation Method for DS-InSAR Deformation Solution
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 3, Pages: 519-530(2026)
- 作者机构：
  
  中南大学地球科学与信息物理学院,湖南长沙，410083
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240125
  CLC： P237
- Received：21 October 2024，
  
  Published：05 March 2026
- 稿件说明：
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李晓田,谢磊,江坤，等.联合M估计和贝叶斯估计的DS-InSAR形变解算方法[J].武汉大学学报（信息科学版）,2026,51(3):519-530.

LI Xiaotian,XIE Lei,JIANG Kun,et al.A Joint M-Estimation and Bayesian Estimation Method for DS-InSAR Deformation Solution[J].Geomatics and Information Science of Wuhan University,2026,51(3):519-530.
李晓田,谢磊,江坤，等.联合M估计和贝叶斯估计的DS-InSAR形变解算方法[J].武汉大学学报（信息科学版）,2026,51(3):519-530. DOI：10.13203/j.whugis20240125

LI Xiaotian,XIE Lei,JIANG Kun,et al.A Joint M-Estimation and Bayesian Estimation Method for DS-InSAR Deformation Solution[J].Geomatics and Information Science of Wuhan University,2026,51(3):519-530. DOI：10.13203/j.whugis20240125

摘要

合成孔径雷达干涉测量（interferometric synthetic aperture radar

InSAR）中的联合永久散射体（persistent scatterer

PS）和分布式散射体（distributed scatterer

DS）估计地表形变，可增加非城市区域的相干目标监测密度。然而，目前仍存在相干目标点数量与参数估计精度难以兼顾的问题，因此，提出了一种联合M估计和贝叶斯估计的DS-InSAR形变解算方法（joint M-estimation and Bayesian estimation-InSAR

MB-InSAR）。所提方法使用三弧段构建PS-DS弧段网，将加权贝叶斯估计和M估计分别应用于不同方差水平的DS点形变参数估计。模拟实验结果表明，相较于传统最大似然估计和普通贝叶斯估计，MB-InSAR对长时间序列的速率估计精度提高了47%，且受PS点密度和噪声的影响较小。在真实实验中，所提方法与3个GPS站点结果比对显示出了良好的一致性。模拟实验和真实实验验证了MB-InSAR方法在分布式目标形变参数估计中的有效性，提高了DS-InSAR技术在复杂地表环境下的形变解算精度，并保证了相干目标点密度，有助于揭示精细地表的形变特征。

Abstract

Objectives

As a deformation monitoring technique based on surface observation

interferometric synthetic aperture radar (InSAR) has been widely used in the study of surface deformation monitoring for its advantages of high precision and wide range. However

there is still a contradiction between the number of measuring points and the measurement accuracy in the common methods of surface deformation monitoring using InSAR

which limits its application in urban low-coherence areas. Distributed scatterer-InSAR (DS-InSAR) has emerged as a powerful remote sensing technique that significantly increases the number of coherent target pixels in non-urban areas without sacrificing spatial resolution. However

there remains a challenge in balancing the quantity of coherent targets with the accuracy of deformation estimation in conventional DS-InSAR method. To address this issue

we propose a new DS-InSAR method that integrates M-estimation and Bayesian estimation (MB-InSAR)

further improving the accuracy of parameter estimation for deformation rates in low-coherence areas.

Methods

The proposed method applies a three-arc strategy to construct the persistent scatterer-disctributed scatterer (PS-DS) arcs network

which provides a more accurate prior distribution for the DS estimator. Subsequently

the MB-InSAR method uses the obtained PS point parameter to provide the prior distribution of the parameters for DS solution. The DS points to be solved are divided into points with different interpolation error levels through the velocity interpolation variance threshold. The DS point parameters with small interpolation error are obtained based on weighted Bayesian estimation

and the DS point parameters with large interpolation error are obtained based on M estimation. The robustness of DS parameter estimation is further improved.

Results

Quantitative evaluation using simulated data shows that the MB-InSAR method can provide more stable and accurate estimation results than maximum likelihood estimation (MLE) and Bayesian estimation for observation noise and uneven distribution of observation points. The MB-InSAR method improves the accuracy of long time series velocity estimation by 47%

compared with traditional MLE and Bayesian estimation. Moreover

the MB-InSAR method exhibits less sensitivity to PS density and noise. The experiments in Katy and Sienna region

Houston city

USA

indicate that the MB-InSAR method increases the number of measurement points by 160% and 125%

respectively. Futhermore

the velocity obtained by the MB-InSAR method shows strong consistency with GPS observation

and the maximum difference between the MB-InSAR method and GPS is less than 2.5 mm/a.

Conclusions

The MB-InSAR method is effective in improving the estimation accuracy for the DS targets

which can shed more light on deciphering the fine-scale deformation characteristics with the complex surface environment.

关键词

Keywords

references

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