Theory of Differential SAR Interferometry Based on Permanent Scatterers and Applications to Shanghai’s Surface Subsidence Detection

Abstract: Differential synthetic aperture radar interferometry(DInSAR) is a newly developed technique for monitoring large-scale ground deformation with some prominent advantages such as high accuracy and pantoscopic view. It therefore can greatly complement many conventional point-based geodetic techniques such as GPS and leveling. This provides a viable space-geodetic approach for ground deformation detection and geophysical studies. However, decorrelations and atmospheric delays impede the applications of DInSAR. The newly-proposed idea, called DInSAR based on permanent scatters (PS-DInSAR), is at present regarded as the most efficient approach in overcoming both decorrelations and atmospheric delays. Nevertheless some problems still exist in the key processing procedures of PS-DInSAR such as PS detection, network construction and model estimation. Thereby, this thesis focuses on investigating basic principles of PS-DInSAR and critical algorithms as well as exploring new approaches so as to improve both accuracy and reliability in PS-DInSAR.The influence of terrain, deformation, atmospheric delays and decorrelations onto interferometric phases is first discussed by analyzing the components of interferometric phases. The principle of DInSAR for deformation detection has been described based on the analysis of phase components. This investigation laid a foundation for modeling differential phases in PS-DInSAR.The accurate co-registration of SAR images is one of key procedures in data processing of PS-DInSAR. The conclusion that the performance of correlation-coefficient method (CCM) ranks first has been drawn by comparing the three accurate co-registration algorithms such as CCM, method based on the average fluctuation function of phase-difference image (MAF), as well as maximum-spectrum method (MSM). CCM is therefore chosen as the approach for co-registering time-series SAR images in PS-DInSAR and Doris software can be utilized for this purpose.To improve accuracy and reliability in identifying PSs, a new PS detection algorithm, called a dual-threshold method based on amplitude information, has been proposed. It considers both PS’s temporal stableness of radar backscattering and its high signal noise ratio (SNR) of radar echoes. This method has been proven effective and more reliable by comparing with other approaches by means of the experiments with 26 ERS-1/2 SAR images of Shanghai.For modeling of PS neighborhood differential phases, it is the first time that the method of creating three-dimensional (3D) PS network is proposed to more precisely determine the neighborhood of PSs. After deriving differential values along arcs in the network, a parametric adjustment method is used to eliminate geometric inconsistency of the 3D network, and thus estimating the terrain error and linear deformation velocity, as well as unwrapping differential phases of PSs. A robust estimation method is also adopted to avoid contamination of possible gross errors during the network adjustment. The investigation on detecting ground subsidence over Lujiazui of Shanghai shows that the results derived from 3D PS network are more accurate than those derived with 2D PS, network created with image planar coordinate system.A new algorithm, called solution-space search (SSS), has been proposed to estimate the parameters in the model of PS neighborhood differential phases. After determining solution-space size, location and search strategy according to some apriori information, the SSS method can be used to compute the optimal parameters rapidly. Besides, the atmospheric phase estimation algorithms for main and slave images have been improved by adding the low pass filtering procedure to raw processing steps. The experiments on detecting ground subsidence over Lujiazui in Shanghai have proven that the SSS method can effectively and reliably estimate the parameters in the model of PS neighborhood differential phases, and the improved algorithms of estimating atmospheric phases are able to mitigate the imapcts of decorrelation noises. Finally, the ground subsidence from 1992 to 2002 over Lujazui area of Shanghai has been successfully detected using a set of PS-DInSAR computer programs which is developed in the Matlab environment. It is found that the maximum and minimum subsidence over this area within 10 years is 183.2mm and 78.4mm, repectively, and the averaged displacement velocity is 13.72mm/a. The results are in good agreement with the ground-based measurements. This indicates that the ground deformations can be efficiently detected and tracked with PS- DInSAR, the algorithms and computer programs developed in this thesis are viable and reliable. Moreover, the statistical analysis on temporal and spatial correlation in terms of deformation and atmospheric phase has been conducted. It can be concluded that the deformations exhibit strong temporal correlation as well as strong spatial correlation within 4km, and the atmospheric delays show strong spatial correlation within 2km without temporal correlation…
Key words: interferometric synthetic aperture radar (InSAR); permanent scatterer (PS); deformation detection; decorrelation; atmospheric delay; modeling and estimating of PS network

This entry was posted in Doctoral Dissertation. Bookmark the permalink.