Abstract:
Persistent Scatterer Interferometry (PSI) is a powerful remote sensing technique that measures and monitors surface deformation over time. This review paper provides an overview of PSI, including its basic principles, algorithms, and applications. We also discuss the challenges of applying PSI to natural terrain, where man-made structures are absent or less prominent.
In this context, we present a new approach to improve the applicability of PS-InSAR techniques to natural terrain. Our method involves increasing the density of Persistent Scatterers (PS) network and improving the reliability of phase unwrapping algorithms.
We demonstrate the efficacy of our new PS selection technique with experimental results from the San Francisco Bay Area, Lyngen region of Norway, and the Central San Andreas Fault. We also present two novel phase unwrapping algorithms and compare the estimated line of sight velocity and deformation time-series with results from other time-series InSAR algorithms.
Background
Time-series InSAR techniques are designed to estimate temporal characteristics of surface deformation by combining information from multiple SAR images acquired over time. These techniques have proven effective in various applications, including urban terrain, where man-made structures act as PS due to their corner reflector-like scattering behavior and high radar reflectivity.
However, the absence of bright man-made structures in natural terrain makes reliable estimation of deformation using traditional PSI-InSAR techniques challenging. The main obstacle is the phase unwrapping stage, where solutions are directly dependent on the PS network density.
Methodology
Our approach involves two main components: increasing PS network density and improving phase unwrapping algorithms.
Firstly, we present an information-theoretic approach to PS pixel selection, which demonstrates the ability of these new algorithms in identifying a denser network of PS in natural terrain. We also address the spatio-temporal (three-dimensional) phase unwrapping problem applicable to sparse and non-uniformly sampled time-series InSAR data sets.
Our novel "edgelist" phase unwrapping algorithm is designed to handle these complex problems, and we demonstrate its efficacy with experimental results from the Central San Andreas Fault region north of Park field, CA.
Results
We provide detailed comparisons of the estimated line of sight velocity and deformation time-series with results from other time-series InSAR algorithms developed by research groups based in NORUT, Norway and IREA-CNR, Italy.
Our results demonstrate the effectiveness of our new PS selection technique and phase unwrapping algorithm in improving the reliability of PSI-InSAR techniques in natural terrain. This approach has significant implications for a wide range of applications, including monitoring geological processes, studying earthquakes, and understanding climate change.
Persistent Scatterer Interferometry is a powerful remote sensing technique that has proven effective in various applications. However, its applicability to natural terrain has been limited due to the absence of bright man-made structures.
Our new approach addresses this challenge by increasing PS network density and improving phase unwrapping algorithms. We demonstrate the efficacy of our method with experimental results from various regions around the world.
This study highlights the potential of PSI-InSAR techniques for monitoring geological processes, studying earthquakes, and understanding climate change in natural terrain. Future research directions include further improving the reliability and accuracy of PSI-InSAR techniques, as well as exploring new applications in various fields.