Research On Some Key Issues Of Satellite Insar Data Processing

As a developing and promising space geodetic technique, satellite synthetic aperture radar interferometry (InSAR) has the advantages of high resolution, all day, all weather, large range and so on. A number of experiments have demonstrated that InSAR is very useful in such fields as digital elevation model (DEM) reconstruction, ground deformation detection, geophysical studies, etc. However, due to the pattern of single-antenna and repeat-pass, the precision and reliability of DEM derived by InSAR would be seriously restricted by four major negative factors, i.e., temporal and geometrical decorrelation, atmospheric delay, topography as well as thermal noise. Therefore, this thesis selects these impact factors as research topics, and explores key theoretics and methods to overcome the effects of thermal noise, decorrelation, atmospheric delay and topography. The motivation of this research is to improve both accuracy and reliability in InSAR DEM and to offer new ideas and approaches for data processing of InSAR. The main research contents and relevant conclusion are as follows:In order to filter InSAR interferogram effectively in wavelet transform domain, the thesis has analyzed the types and characteristics of interferogram noises in terms of the energy and distribution of wavelet coefficients. The results are as follows:(1) there is only additive noises without multiplicative noises in interferogram. (2) The distribution regularity of remarkable coefficients in high-frequency sub-bands is not random but clustering distribution, and these remarkable coefficients have some correlation with in the same scale or among different scales, whereas the distribution regularity of the wavelet coefficients corresponding to noises is random distribution without any correlation; (3) The wavelet coefficients corresponding to additive noises obey standard normal distribution.According to the standard normal distribution of interferogram noises in wavelet domain, an algorithm of wavelet-wiener combined (WWC) filter is proposed by utilizing the merits of wavelet transform and wiener filtering. Due to the trait that the wavelet coefficients corresponding to interference signals in a same wavelet scale have some relativity, this thesis proposes an algorithm of filtering InSAR interferogram that is based on wavelet phase analysis. For validating the effects of two algorithms, two C-band interferograms, i.e., high-SNR one over Phoenix, USA and low-SNR one over Bam, Iran are selected to carry out the experiments of filter and analysis, and the results indicate that not only to the high-SNR interferogram, but also to the low-SNR interferogram, the two algorithms can both suppress noises successfully.The effects of radar propagation and interferometric phases caused by atmospheric delay are analyzed, and the results show that the bigger pressure and humidity are, the bigger retardation of radar propagation is, and for interferometric phase, the deviation caused by atmospheric pressure change is much more less than that caused by water vapor change. In addition, according to the "1/f" characteristic of atmospheric delay in frequency domain, the value of noises and atmospheric influence of six interferograms that selected for this experiment are estimated by using the wavelet model of 1/f processes.Compared to the SRTM DEM, the InSAR DEMs are evaluated to determine the impacts of terrain slope and aspect on elevation accuracy. The results show that there is an obvious relativity between InSAR DEM accuracy and terrain slope. When terrain slope is less than 10°, the deviation of DEM changes little and the accuracy is high. The DEM accuracy degrades almost linearly with increasing slope when the terrain slope is between 10°snd 30°. The accuracy of steep slopes over 30°is unacceptable. However, the rule between the InSAR DEM accuracy and the terrain slope is hardly found.InSAR DEMs are evaluated to determine the impacts of spatial baseline and time baseline on elevation accuracy. In certain extent, the longer spatial baseline and vertical baseline are, the higher accuracy is. Moreover, when the lengthes of all spatial baselines are equivalent, the longer vertical baseline is, the higher accuracy of DEM is. In terms of temporal impact, the DEM accuracy is more or less related to the time intervals of the InSAR pairs, especially for vegetation regions. Because the changes of vegetation growth seriously affect radar echo signal and lead to the accuracy reducing of DEM.To weaken the influence of noises, atmospheric delay and spatial baseline, a multi-baseline InSAR DEM fusion algorithm based on wavelet transform is proposed. After eliminating the influence of spatial baseline, this algorithm realizes the multi-baseline InSAR DEM weighted fusion on the basis of that the values of interferometric phase caused by noises and atmospheric delay are estimated by using the wavelet model of 1/f processes. Six InSAR DEMs over Phoenix, USA are selected to carry out the experiments of fusion, and the results indicate that this algorithm can effectively reduce the influence of noises, atmospheric delay and spatial baseline, and acquire DEM with higher accuracy.