Deformation Monitoring By Multi-Temporal Insar With Both Point And Distributed Scatterers

Land subsidence is a destructive engineering geologic phenomenon. It can cause damage to public facilities, resource development, and even introduce indwelling which is responsible for salinization of soil and fresh water, invalidation of harbor facilities. as well as submergence of coastal areas. Three main tools can be used to detect land subsidence. They are traditional method such as leveling measurement, global positioning system (GPS). and differential interferometric synthetic aperture radar (DInSAR). Leveling measurement possesses the highest accuracy but the lowest spatial density of the measurement points. GPS is capable of providing measurement points in large spatial scale. However. the cost is too high to afford a high density for deformation field. DInSAR. which provides large spatial scale and high density of measurement points, has perfectly overcome the shortcomings of the aforementioned two technologies. Deformation field with high spatial resolution is able to be extract from the DInSAR technology. Therefore, DInSAR is used more and more frequently in land subsidence monitoring.However. DInSAR is limited by some technical flaws. First. DInSAR is sensitive to spatio-temporal decoherence. The spatio-temporal decoherence makes the signals not able to interfer. Therefore, it is difficult to detect deformation information in low coherent areas. Second, accuracy of DInSAR is limited to accuracy of phase unwrapping algorithms. Phase unwrapping is not robust in low coherent areas. The results of those areas are therefore biased and not reliable. They can’t be used for in-depth analysis. Third, procedures of DInSAR are so complex that errors in the final results may originate from any step of the procedures. The errors may be caused by the image coregistration, orbital error, and atmospheric phase screen, et al. Therefore some scholars have proposed the multi-temporal InSAR (MTInSAR) to provide more reliable deformation information.Persistent scatterer InSAR (PSI) is one of the most popular MTInSAR methodologies. PSI concentrates on the persistent scatterer (PS) instead of all kinds of the ground coverage. At least 30 SAR images are suggested to use to make the PS pixels statistically reliable. Because the PS pixels keep coherent in a long time, with little effects of spatio-temporal decoherence and noise components, they are reliable in providing reliable results for deformation field. Some typical PSs include street lamps, rocks, electricity pylons, and dihedral corners in residential regions. Although the sizes of the PSs in SAR images are less than one pixel, the backscattering of the PSs occupies most of the backscattering information of the sub-elements within a single pixel, bringing high signal-to-noise ratio (SNR) by suppressing the noise components. The standard procedures of PSI follow two main steps. First, the relative deformation parameters have to be calculated. Then the relative deformation parameters have to be converted to the absolute ones with respect to a common reference point. Finally, the nonlinear deformation components are extracted using spatio-temporal filtering. The final deformation time series are calculated by adding the nonlinear deformation components to the linear ones. By following the procedures of PSI, the precision is increased compared to InSAR. Besides, the character of large spatial scale of measurement points is kept in PSI. In-depth analysis shows that the accuracy of PSI can reach up to submillimeter level if the SNR of PS pixels are high enough. This is an encouraging conclusion which provides authentic basis for engineering application of PSI.PSI provides high reliability to the deformation information. However, the density of PS pixels is lower than that of InSAR. Therefore. spatial resolution of deformation field is too low to provide deformation details. It is reported that 90% of the ground coverage is distributed scatterer (DS). Therefore, DS is potential in supplying deformation details in MTInSAR analysis. Unlike PSs. DSs are large in physical sizes. They occupy tens of pixels in SAR images. They are affected by spatio-temporal decoherence, but pixels with high SNR can also be detected among DS pixels. Those pixels are useful in providing reliable deformation information. Some typical DSs include road, roofs, bare lands, idle farmlands, waste mountains, desserts, et al. Although the DS pixels are affected by spatiotemporal decoherence, they still have moderate coherence values. They are stable in time series. A typical DS detection methodology is SqueeSARTM which obtains at least a 4 times larger density than PSInSARTM. At the suburban areas, the density can be as large as 10 times. However, SqueeSARTM calculates both PS and DS pixels jointly. Error propagation from DS pixels to PS pixels lowers the quality of the latter ones. Thus the results cannot be ensured. Therefore in this thesis we propose a hierarchical point analysis (HPA) methodology to detect the DS pixels and calculate the corresponding deformation parameters.The HPA uses amplitude dispersion index (ADI) to classify all the pixels to several groups. Pixel-by-pixel strategy is applied to analyze each of the pixels, thus extracting both PS and DS pixels. To make the result authentic, the PS pixels are first calculated. Then the groups are maintained by considering ADI intervals. And DS pixels are extracted after treating the groups one by one. It is worth stating that each DS candidate is assessed by using the valid pixels extracted from the previous groups. Only those that have passed the quality assessments are maintained as DS pixels.Besides, the thesis proposes a region growing algorithm and phase gradient-based nonlinear deformation components extraction method to accomplish the processing chains of the MTInSAR. The region growing method is used to convert the relative deformation parameters to the absolute ones. This method is applied to conduct kinds of logical judgments to take place of least square adjustment. It is efficient in providing high accuracy. And low computing resources are occupied by comparing to least square adjustment. The phase gradient-based nonlinear deformation components extraction method applies no phase unwrapping algorithm. It uses the phase gradient to do spatio-temporal filtering and get the nonlinear deformation gradients on arcs. Thereafter, the integral paths maintained after region growing are used to provide integration for those gradients and get the deformation values for each point. This method is reliable by avoiding phase unwrapping errors.Finally, the TerraSAR-X images acquired over Xiqing district, Tianjin. China is applied to validate the algorithms proposed in the thesis. Meanwhile, the leveling data obtained during the same observation period are introduced for comparison purpose. Experimental results show that the root-mean-square error of subsidence rates provided by HPA is 2.5 mm/yr. The nonlinear subsidence components are extracted using the phase-gradient based algorithm, thus calculating the subsidence time series. The subsidence time series are compared to the corresponding leveling results. Results show that the RMSE of accumulated subsidence values are 3.8 mm. Those are evidences that the proposed methodologies are validated in providing accurate deformation information. Besides, the thesis has conducted experiments by using the TerraSAR-X images acquired from Baoshan district, Shanghai as well as Hong Kong International Airport (HKIA). Experimental results show that the proposed methodologies are able to provide detailed deformation information along the railways in Baoshan district, Shanghai. Besides, the detailed deformation information provided for HKIA shows high consistency with the geology information in the study area. Those are the evidences that the proposed methodologies are validated in providing high spatial resolution for the useful measurements. The proposed MTInSAR algorithms are expected to facilitate deformation monitoring in engineering applications.