Improvement of Persistent Scatterer Interferometry to Detect Large Non-Linear Displacements with the 2π Ambiguity by a Non-Parametric Approach

Abstract

Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor's wavelength (2.5λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is −0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.

Implementation of Non-Linear Non-Parametric Persistent Scatterer Interferometry and Its Robustness for Displacement Monitoring

“…In our previous study [ 22 ], the height s 0 for the displacement estimation was decided by the maximum temporal coherence in the given EV spectrum. This method works properly for many types of non-linear displacements.…”

“…In our previous study, we proposed an extended PSI method, called non-parametric non-linear PSI (NN-PSI) [ 22 ], and the non-linear displacement, which the conventional PSI (ConvPSI) wrongly estimates, was appropriately estimated. NN-PSI is supposed to estimate many types of the displacements by using the complete velocity spectrum in the elevation-velocity (EV) spectrum, whereas ConvPSI only uses single frequency when reconstructing displacement phase.…”

“…In [ 22 ], the subsidence influenced by the subway construction in a short period in Budapest was successfully estimated by NN-PSI. However, only one type of non-linear displacement was validated, and it is necessary to examine if NN-PSI is also applicable to other types of non-linear displacements.…”

“…The NN-PSI approach was proposed in [ 22 ], and the main concept is summarized in this subsection. NN-PSI is based on the single-master and multi-baseline model [ 23 , 24 , 25 , 26 , 27 ], illustrated in Figure 2 .…”

Separating and Unwrapping of Residual Topographical and Time-Series Deformation Phases in PS-InSAR Based on Zero-Temporal Baseline Combination and Time-Domain Differentiation

“…Nonmodeled deformation often partially lose during the temporal-spatial filtering process, and the real deformation cannot be fully restored by these parameterized methods [19]. To avoid the effect of badly underfitted deformation models, some nonparametric methods have been proposed [20][21][22], but these methods are complex and can exhibit low robustness. Therefore, separating, unwrapping the phase and extracting deformation time series accurately and efficiently remains a key issue in further advancing PS-InSAR applications.…”