Differential radar interferometry (DInSAR) has demonstrated its ability to monitor ground deformation. DInSAR uses two synthetic aperture radar (SAR) images acquired over the same region to generate a differential interferogram. A continuous ground displacement map with large coverage can be extracted from a high quality differential interferogram, which makes DInSAR extremely competitive compared to traditional ground survey techniques. DInSAR has also been recently extended to monitor the temporal evolution of ground deformation through the use of advanced DInSAR techniques. Such techniques make use of stacked differential interferograms to generate accurate deformation time series. Nowadays, in order to achieve higher resolution and wider spatial coverage, large sized SAR datasets are increasingly used in DInSAR applications. Unfortunately the significantly increased size of the datasets causes many difficulties in DInSAR processing. In this dissertation a series of algorithms have been developed in order to address the DInSAR processing problems caused by large data file sizes. To make large dataset processing more automated, an improved DEM coregistration strategy has been designed. Compared to the conventional automation method, it has an improved efficiency and a higher accuracy, permitting large datasets to be processed more smoothly. To break the computational bottleneck of large dataset processing, a two-stage optimisation (TSO) phase unwrapping algorithm has been developed. The TSO algorithm resolves the block partition and parallelisation problems in phase unwrapping. To improve the uniformity of multi-track differential interferograms, an orbit error compensation approach has been proposed. It enables the fringe pattern inconsistency induced by orbit error to be eliminated, resulting in a better interpretation of multi-track DInSAR observations. To reduce the required storage resources in deformation time series analysis for large datasets, a new advanced DInSAR method has been designed and implemented. This method eliminates unnecessary disk space consumption and I/O operations, making it possible for the deformation time series analysis to be economically applied to large datasets. On the whole, the proposed methods enable DInSAR techniques to be better applied to large datasets. It is believed that they will stimulate the further advancement of DInSAR.