IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Xinyao Sun,Parwant Ghuman,Aaron Zimmer,Subhayan Mukherjee,Irene Cheng

doi:10.3390/rs13132615

Abstract

Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

Highlights

Over the years, there has been an increasing interest in interferometric synthetic aperture radar (InSAR) techniques
We apply an industry 3vGeomatics’s industry-standard InSAR processing pipeline [40,51] to perform data preprocessing as well as generate the reference results to assess the performance of the proposed iterative grid search (IGS)-covariance matrix adaptive evolution strategy (CMAES) method on real data
We provided two main contributions: (1) designing a two-stage architecture suitable for interferometric phase processing and (2) introducing a benchmark hybrid simulation dataset by combing real-world baseline parameters and synthetic ground truth signals for an effective evaluation

Summary

Introduction

There has been an increasing interest in interferometric synthetic aperture radar (InSAR) techniques. An InSAR interferogram represents the phase difference between two SAR images, taken at different temporal times looking at the same ground location on Earth. It has provided significant advances in measuring the Earth’s surface deformation and creating precise digital elevation models (DEM). InSAR applications focused on analyzing a single interferogram derived from a pair of SAR images [1,2]. Time-series InSAR (TSInSAR) techniques have emerged as a powerful strategy to monitor slow and subtle terrain displacements [3]

Methods

Results

Discussion

Conclusion