Studies On The InSAR Missing Data Fitting

InSAR technology has become an important means of monitoring the changes of the surface because of its high precision, high resolution and other characteristics and it is widely used in the study of geological disasters such as coal mine, earthquake, volcano, land subsidence monitoring and many other fields. However, due to the influence of time or spatial decorrelation and other factors, there are often some missing information in InSAR extraction,. So it is necessary to fill the missing information.For fitting and filling the missing data, spatial data interpolation method is commonly used. After comparing and analyzing the commonly used interpolation methods, this paper mainly discusses the collocation method which can not only take into account the structural components but also better express the local surface change. Based on the study of the fitting of the anisotropic random signal covariance function, the author puts forward the anisotropic adaptive collocation and applies all these data fitting methods to the fitting of InSAR missing monitoring data. The main achievements are as follows:1. This paper discusses the error source which has an effect on the InSAR monitoring results and the decorelation source which affects InSAR coherence and results in missing data. This paper also analyzes the influencing factors of missing data fitting precision and finds out that in addition to the fitting method which affects the fitting precision, the size of the missing data area and the fluctuation of the missing data are two key factors to the fitting precision.2. This paper discusses the characteristics and properties of conventional fitting methods, such as the polynomial fitting, IDW and kriging in the application of missing data fitting and filling. Considering the characteristic of anisotropy in reality which is often considered to be isotropic in the conventional collocation method, this paper puts forward the anisotropic adaptive collocation method which is based on the analysis of anisotropic covariance function fitting, and verifies the effectiveness of the method by applying it into the fitting of InSAR missing monitoring data3. This paper comparatively analyzes the accuracy of different fitting methods through the fitting of the surface subsidence and earthquake’s deformation missing data and finds out that the polynomial surface fitting method’s precision is too low to properly fit and fill the missing data. Although the inverse distance weighted method is simple and easy to implement, it is easy to cause the phenomenon of over smoothing fitting when fitting the large area missing data. The kriging and collocation method have higher fitting precision. The subregional fitting has a higher filling accuracy for the deformation which has obvious regional characteristics.