A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

Rui Zhang,Bing Yu,Hui Lin,Guoxiang Liu,Zhilin Li,Guo Zhang,Xiaowen Wang

doi:10.3390/rs70100211

Abstract

This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI) for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution) is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data.

Highlights

As a newly arisen space geodetic technique, synthetic aperture radar interferometry (InSAR) is well known as an effective and powerful tool for monitoring land deformation
persistent scatterer interferometry (PSI) solution are ±2.5 and ±2.4 mm, respectively. This means that the subsidence time series derived from the hierarchical (i.e., global control network (GCN)-localized triangular irregular network (LTIN)) PSI solution is in good agreement with the leveling measurements and those derived from the global-triangular irregular network (TIN) PSI solution
For the purpose of balancing between computational efficiency and solution accuracy, this paper presents a hierarchical approach in PSI, i.e., two levels of persistent scatterer (PS) networking and solution, for estimating deformation rates with time series of high-resolution satellite SAR images

Summary

Introduction

As a newly arisen space geodetic technique, synthetic aperture radar interferometry (InSAR) is well known as an effective and powerful tool for monitoring land deformation. PSs. the differential modeling of phase data can be implemented for every arc to cancel out or decrease the impact of spatially-correlated atmospheric delay and other systematic errors [7,8,9,10], benefiting deformation extraction by a least squares (LS) estimator. Several types of PS networks, including star network, minimum spanning tree (MST) network, triangular irregular network (TIN), and freely connected network (FCN), have been used for differential modeling and time-series deformation analysis. It is understood that more connections between adjacent PSs in a network can result in a more reliable solution of deformation measurements by the LS adjustment, but decreases computational efficiency. High density of PSs for deformation analysis benefits from high-resolution

Methods

Results

Conclusion