Noise separation and refinement for InSAR deformation time series by considering sequence non-stationarity

Ground deformation time series monitoring is one of the most important tasks of multi-temporal interferometric synthetic aperture radar technique (MT-InSAR). The deformation time series obtained by MT-InSAR technique need to be further refined because of the effect of noise error. Due to the variety of deformation types, the traditional filtering methods are prone to the problems of the inapplicability of single model, difficult to determine the filtering window, difficult to evaluate filter accuracy, etc. In this paper, we propose a new method to separate noise and refine deformation time series in large-scale areas by considering sequence non-stationarity. We build a model library for large-scale area which contain common ground deformation model and then twice fitting process are performed by considering goodness of fit and time series stationarity to realize the transformation of sequence from non-stationary to stationary. Finally, a large-window filtering is used to extract residual deformation signal and the whole deformation time series can be obtained precisely. Comparison experiments for both regular deformation time series and irregular deformation time series were performed by using traditional triangular filter and new method. Experimental results show that there is an improvement of 40.69% for separation accuracy in regular deformation time series and an improvement of 27.54% in irregular deformation time series. Furthermore, the new method has more superiority in filter window selection, noise handling ability and accuracy assessment.