東華大學圖書館 |

Statistical Theory for the Detection of Persistent Scatterers in Insar Imagery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Theory for the Detection of Persistent Scatterers in Insar Imagery./
作者:	Huang, Stacey Amy.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	139 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Geophysics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812951
ISBN:	9798494454478

Statistical Theory for the Detection of Persistent Scatterers in Insar Imagery.
Huang, Stacey Amy.

Statistical Theory for the Detection of Persistent Scatterers in Insar Imagery. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 139 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

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.Here, we identify and address two issues in current PS-InSAR implementation. First, much previous understanding of the relationship between PS density, image resolution, and terrain type has been rather qualitative. Deeper knowledge of PS statistics and their relation to image resolution and terrain is necessary if we wish to design ecient systems for future satellite missions, as well as designing more e↵ective PS detection schemes. Second, PS-InSAR techniques have largely relied on statistical frameworks that do not accurately describe the backscattered energy. PS detection theory to date has largely been based on Gaussian-derived models, which have been shown to be ine↵ective in describing returns from high-resolution SAR imagery. This suggests that PS detection theory operates on suboptimal procedures, and improved detector design could enable high-sensitivity deformation mapping even over traditionally challenging terrain.The work in this thesis is presented in three major parts. First, we analyze PS density for di↵erent 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. We find that the increase in PS density is proportional to increased bandwidth due to a higher pixel density in finer resolution images. Second, we characterize the probability distribution functions (PDFs) of the backscatter from both PS and non-PS (clutter). We find that contrary to the models that are applied in current PS detection models, PDFs of both PS and clutter are highly non-Gaussian, even over a variety of bandwidths and wavelengths. Finally, we demonstrate a novel PS detector based on applying a non-Gaussian extension to an existing PS detector, and we present simulations to select the threshold of the detector in a more rigorous fashion than in previous work. We show results of applying the improved detector over two regions with di↵erent terrain: 1) urban and lava flow terrain in Hawaii, and 2) a small city and agricultural fields in 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 Gaussian detector, the small baseline subset (SBAS) InSAR method, and GPS.

ISBN: 9798494454478Subjects--Topical Terms:

535228
Geophysics.

Statistical Theory for the Detection of Persistent Scatterers in Insar Imagery.
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520 $a 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.Here, we identify and address two issues in current PS-InSAR implementation. First, much previous understanding of the relationship between PS density, image resolution, and terrain type has been rather qualitative. Deeper knowledge of PS statistics and their relation to image resolution and terrain is necessary if we wish to design ecient systems for future satellite missions, as well as designing more e↵ective PS detection schemes. Second, PS-InSAR techniques have largely relied on statistical frameworks that do not accurately describe the backscattered energy. PS detection theory to date has largely been based on Gaussian-derived models, which have been shown to be ine↵ective in describing returns from high-resolution SAR imagery. This suggests that PS detection theory operates on suboptimal procedures, and improved detector design could enable high-sensitivity deformation mapping even over traditionally challenging terrain.The work in this thesis is presented in three major parts. First, we analyze PS density for di↵erent 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. We find that the increase in PS density is proportional to increased bandwidth due to a higher pixel density in finer resolution images. Second, we characterize the probability distribution functions (PDFs) of the backscatter from both PS and non-PS (clutter). We find that contrary to the models that are applied in current PS detection models, PDFs of both PS and clutter are highly non-Gaussian, even over a variety of bandwidths and wavelengths. Finally, we demonstrate a novel PS detector based on applying a non-Gaussian extension to an existing PS detector, and we present simulations to select the threshold of the detector in a more rigorous fashion than in previous work. We show results of applying the improved detector over two regions with di↵erent terrain: 1) urban and lava flow terrain in Hawaii, and 2) a small city and agricultural fields in 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 Gaussian detector, the small baseline subset (SBAS) InSAR method, and GPS.
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