Statistical theory for the detection of persistent scatterers in insar imagery
Abstract/Contents
- Abstract
- Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique for observing subtle deformation of the Earth's surface over time through multiple observations of the same ground area. Because radar backscatter depends on wavelength-scale properties of surfaces, traditional InSAR methods can fail over naturally changing terrain. The persistent scatterer InSAR (PS-InSAR) technique is one important extension for time-series analysis which identifies and utilizes only the most reliable points in InSAR images for analysis. PS-InSAR has been successfully applied to detect mm-level deformation associated with natural hazards such as earthquakes, volcanoes, and landslides. To date, however, the implementation of PS-InSAR has not been fully optimized, which can limit its utility in challenging mixed-terrain regions. In this thesis, we show that these techniques can be further optimized by characterizing the statistics of PS and developing a statistical framework for applying PS-InSAR techniques. There are three major parts to this work. First, we analyze PS density for different terrain types and image resolution and present a model for predicting the change in PS density, which adheres to empirical results within 50% error and closer for points that form the desired network for PS. Second, we characterize the probability distribution functions (PDFs) of the backscatter from PS and non-PS (clutter) and find that both are highly non-Gaussian over a variety of bandwidths and wavelengths. Finally, we demonstrate a novel maximum likelihood PS detector based on these non-Gaussian models. We show results from the improved detector over Hawaii's Kilauea Volcano and California's Central Valley. In both areas, the non-Gaussian detector finds many more PS than in the existing detector, which leads to a more complete map of deformation. Further, we find that the retrieved deformation time-series is consistent with that measured with three other methods: the existing maximum likelihood Gaussian detector, the small baseline subset (SBAS) InSAR method, and GPS.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Huang, Stacey Amy |
|---|---|
| Degree supervisor | Zebker, Howard A |
| Thesis advisor | Zebker, Howard A |
| Thesis advisor | Gao, Grace X. (Grace Xingxin) |
| Thesis advisor | Nishimura, Dwight George |
| Degree committee member | Gao, Grace X. (Grace Xingxin) |
| Degree committee member | Nishimura, Dwight George |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Stacey Amy Huang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/mh468vh5576 |
Access conditions
- Copyright
- © 2021 by Stacey Amy Huang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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