| In recent years, with the rapid growth of economy, industrialization, urbanization and population, the whole world is consuming a huge number of resources. Many kinds of underground resources, underground water, coal, oil and gas, are mined to meet the social requirement. These mine activities has caused devastating ground surface deformation. It is very important to research on how to monitor, analyze and mitigate the ground surface deformation rapidly and efficiently. InSAR is a remote sensing technique for topographic mapping and ground surface deformation monitoring which was developed in recent decades. In order to monitor the ground surface deformation caused by continuous underground resources mine activities, various time-series SAR data processing methodologies and algorithms are developed in this thesis according to two types of ground surface deformation: linear and non-linear.A high-accuracy baseline estimation algorithm based on the Interferometric phases is developed to enhance the accuracy of InSAR technique. One ALOS PASAR pair is used to evaluate this algorithm, and the result is quite convincing.The advanced PSInSAR method based on the adaptive estimation of PS points is developed and applied to monitor the linear ground surface deformation caused by underground water extraction around Beijing. Two SAR images dataset, ENVISAT ASAR and ALOS PALSAR, are used for experimental research. The PSInSAR results fit quite well with the underground water level data.The multiple time-series differential interferograms processing strategy is discussed and applied to monitor the non-linear mine subsidence in Australia. The DInSAR results match quite well with the mine schedule. The accuracy of the DInSAR results is evaluated by the GPS surveying data; it is found that the accuracy of DInSAR results is up to cm level, which is lower than the mm level GPS data.There are 63 figures, 18 tables and 119 references.
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