Partially correlated persistent scatterer theory and techniques for radar interferometry
Abstract/Contents
- Abstract
- Interferometric synthetic aperture radar (InSAR) is an effective technology for measuring temporal changes of the Earth's surface. By combining SAR echoes collected at varying times and orbit geometries, we can produce wide coverage images of crustal deformation fields with centimeter-level accuracy. However, conventional InSAR techniques often fail to recover a coherent deformation signal when the radar imaging geometry or surface scattering properties change significantly between radar passes. This phenomenon, known as decorrelation, produces a random phase term that obscures the deformation signal and reduces the amount of InSAR data suitable for time series analysis. We can overcome these limitations by exploiting a subset of intrinsically phase-stable pixels, the so-called persistent scatterers (PS). Identifying such pixels is a crucial component of this analysis, since phase unwrapping and subsequent deformation estimation on the spatially sparse PS network depends on both pixel selection accuracy and the network density. PS techniques have been shown to work well in urban areas with many strong, stable reflectors, but identifying an appropriate network of pixels in natural or vegetated terrain remains a challenge due to other spatiotemporally varying phase terms. In this dissertation, we present new theory and techniques for generalized PS analysis based on partially correlated persistent scatterers (PCPS): those that are non-ideal but stable enough for deformation time series measurement in largely decorrelated areas. We develop a new physically-based method for modeling spatiotemporal decorrelation, as well as a comprehensive statistical characterization of the resulting interferometric pixels. We show that these analytical results lay a theoretical foundation for PCPS algorithm development. Next, we introduce a more reliable PS selection technique that combines the full set of interferometric observations as a function of their acquisition intervals. The PCPS technique achieves a better trade-off between pixel selection accuracy and network density compared to other PS identification methods. Finally, we present examples of deformation measurements obtained using PCPS analysis. These results demonstrate that through improved statistical characterization, the PCPS technique attains reliable deformation measurements for a variety of wavelengths, terrain, and geophysical processes.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Lien, Jaime |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Zebker, Howard A |
| Thesis advisor | Zebker, Howard A |
| Thesis advisor | Montanari, Andrea |
| Thesis advisor | Segall, Paul, 1954- |
| Advisor | Montanari, Andrea |
| Advisor | Segall, Paul, 1954- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jaime Lien. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Jaime Lien
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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