Evaluation of polarimetric capabilities of dual polarized Sentinel-1 and TerraSAR-X data to improve the PSInSAR algorithm using amplitude dispersion index optimization

Azadnejad, S.; Maghsoudi, Yasser; Perissin, Daniele

doi:10.1016/j.jag.2019.101950

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…Almost at the same time, an advanced PolPSI-COH technique [34] and the coherency matrix decomposition (CMD) method (which can be used as both PolPSI-ADI and POlPSI-COH) [36] have been proposed and evaluated on Sentinel-1 PolSAR images over San Fernando Valley and Mexico city, respectively. These studies have demonstrated the effectiveness of PolPSI with Sentinel-1 PolSAR images, and researchers have shown an increased interest in PolPSI techniques recently [39,42,43].…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

et al. 2022

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With the launch of the Sentinel-1 satellites, it becomes easy to obtain long time-series dual-pol (i.e., VV and VH channels) SAR images over most areas of the world. By combining the information from both VV and VH channels, the polarimetric persistent scatterer interferometry (PolPSI) techniques is supposed to achieve better ground deformation monitoring results than conventional PSI techniques (using only VV channel) with Sentinel-1 data. According to the quality metric used for polarimetric optimizations, the most commonly used PolPSI techniques can be categorized into three main categories. They are PolPSI-ADI (amplitude dispersion index as the phase quality metric), PolPSI-COH (coherence as the phase quality metric), and PolPSI-AOS (taking adaptive optimization strategies). Different categories of PolPSI techniques are suitable for different study areas and with different performances. However, the study that simultaneously applies all the three types of PolPSI techniques on Sentinel-1 PolSAR images is rare. Moreover, there has been little discussion about different characteristics of the three types of PolPSI techniques and how to use them with Sentinel-1 data. To this end, in this study, three data sets in China have been used to evaluate the three types of PolPSI techniques’ performances. Based on results obtained, the different characteristics of PolPSI techniques have been discussed. The results show that all three PolPSI techniques can improve the phase quality of interferograms. Thus, more qualified pixels can be used for ground deformation estimation by PolPSI methods with respect to the PSI technique. Specifically, this pixel density improvement is 50%, 12%, and 348% for the PolPSI-ADI, PolPSI-COH, and POlPSI-AOS, respectively. PolPSI-ADI is the most efficient method, and it is the first choice for the area with abundant deterministic scatterers (e.g., urban areas). Benefitting from its adaptive optimization strategy, PolPSI-AOS has the best performances at the price of highest computation cost, which is suitable for rural area applications. On the other hand, limited by the medium resolution of Sentinel-1 PolSAR images, PolPSI-COH’s improvement with respect to conventional PSI is relatively insignificant.

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Section: Introductionmentioning

confidence: 99%

Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

et al. 2022

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“…(1) There are many state-of-the-art methods for filtering random noise and suppressing atmosphere components from a stack of interferograms [25,27,[32][33][34]. (2) Recently, satellite facilities can provide accurate enough orbits for practical usage [35][36][37]. (3) It is very common to divide a complicated optimization problem into sub-problems, which are easier to be solved than the original problem [38,39].…”

Section: Proposed Methods 221 Definition Of Optimization Problemmentioning

confidence: 99%

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

Sun¹,

Zimmer²,

Mukherjee³

et al. 2021

Remote Sensing

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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.

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“…The interferograms were then geocoded to align them in the correct geographical position for interpretation purposes. The initial selection of PS points was based on noise characteristics and amplitude analysis, as discussed by [35]- [41]. These PSs can only be estimated after eliminating atmospheric artefacts, DEM errors, deformation due to terrain and orbital ramps.…”

Section: Ps-insar Processing Methodologymentioning

confidence: 99%

Estimation of Ground Vertical Displacement in Landslide Prone Areas Using PS-InSAR. A Case Study of Bududa, Uganda

Makabayi¹,

Musinguzi²,

Otukei³

2021

IJG

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Evaluation of polarimetric capabilities of dual polarized Sentinel-1 and TerraSAR-X data to improve the PSInSAR algorithm using amplitude dispersion index optimization

Cited by 18 publications

References 22 publications

Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

Polarimetric Persistent Scatterer Interferometry for Ground Deformation Monitoring with VV-VH Sentinel-1 Data

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

Estimation of Ground Vertical Displacement in Landslide Prone Areas Using PS-InSAR. A Case Study of Bududa, Uganda

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