Study On The Method Of The Modeling, The Detection And The Parameter Inversion For The Mono/bi Static Polarimetric SAR Interferometry

Polarimetric SAR Interferometry (Pol-InSAR) is an advanced SAR system, which combines both the advantages of the Polarimetric SAR and those of the interferometric SAR. It can get the interferometric information of many different polarimetric SAR, so it has been widely used in the domains of vegetation parameters inversion and moving target detection. The model of the Pol-InSAR complex correlation coefficient is the base of the information extraction and decides the precision of the information extraction. Especially, the study on the Pol-InSAR system has been changed from the mono-static mode to the bi-static mode. In this dissertation, the mono-static Pol-InSAR system means that both radars transmit the radar signal and receive its signal at the same time. The bi-static Pol-InSAR system means that only one of the radars transmits the signal and both radars receive the signal. The bi-static Pol-InSAR system is a developing trend of the radar and is a study hotspot in the current domain of radar remoting sensing.This dissertation focuses on the vegetation parameters inversion and the moving target detection by the mono/bi -static Pol-InSAR. The research topics include the highly precise estimation of the Pol-InSAR covariance matrix, the modeling of the mono/bi -static Pol-InSAR complex correlation coefficient of the two-layer vegetation structure and the new parameter inversion method to it, the modeling of the mono/bi -static Pol-InSAR complex correlation coefficient of the three-layer vegetation structure and the new parameter inversion method to it, the method to improve the detection performance of the moving target by the fully-polarimetric along-track interferometry SAR system and the optimization method to the along-track Pol-InSAR system. The main work of this dissertation includes:Chapter 2 introduces the base of the Pol-InSAR. The receive-transit signal model of the mono/bi -static fully-polarimetric LFM signal and the mono/bi- static fully-polarimetric SAR image signal model are established firstly. Then, based on the fully-polarimetric SAR image signal model, the generalized Pol-InSAR signal model is proposed. Finally, based on the signal model of the vegetation parameters inversion, the basic principle of vegetation parameters inversion is expatiated and the effect to the performance of the three-stage inversion method is analyzed.In chapter 3, the method to the highly precise estimation of the Pol-InSAR covari ance matrix is investigated. A novel method based on the adaptive neighborhood region-growing principle and the compensated phase is proposed. Firstly, the bad effect by the phase nonstationarity is eliminated by the way in which the interferometric matrix is compensated with the interferometric phase estimated from the polarimetric interferogram with the best interferometric performance. Then, the region-growing principle is used to the fully-polarimetric SAR intensity images to get a window, in which the pixels satisfy the local scattering stationarity hypothesis. To the window satisfying the local phase stationarity hypothesis and the local scattering stationarity hypothesis, a novel iterative method based on the Hermitian product model is proposed. This new method could overcome the estimation errors caused by the nonstationarities both in the phase and in the scattering intensity, furthermore, it takes into account the characteristics of the Hermitian model, so it has a high estimation precision. Experimental results validate the superiority of this method.In chapter 4, the two-layer vegetation Pol-InSAR correlation coefficient model to the bi-static Pol-InSAR system and the vegetation parameters inversion method to it are studied. A new model to the two-layer vegetation complex correlation coefficient of the bi-static Pol-InSAR system is established. Based on this new model, the effect of scattering components and the receive-transit mode of the interferometric radar to the complex correlation coefficient distribution is analyzed, furthermore, the effect of scattering components and the receive-transit mode of the interferometric radar to the inversion precision of three-stage method is studied. The reason why the inversion precision of the three-stage method is low is discovered. A new vegetation parameter inversion method based on the BP neural network is proposed. This new method uses the BP neural network to fit the nonlinear mapping relationship between the complex polarimetric correlation coefficients and the vegetation parameters. It not only reduces the error caused by the error in the estimations of the ground interferometric phase and the volume correlation coefficient, but also reduces the error caused by the scattering model error. Because this method is not affected by the distribution characteristics of complex correlation, this new method is suitable to vegetation parameter inversion by bi-static Pol-InSAR working in one-transmit bi-receive mode. A Novel vegetation parameter inversion method based on the Freeman decomposition is proposed. Based on the relationship among the polarimetric covariance matrix, the polarimetric interfero- metric matrix and the Freeman decomposition, the new method models the problem of the vegetation parameter inversion as a nonlinear optimization problem, the variables of which are the vegetation parameters. Compared to three-stage inversion process, this new method supplies a new approach to the inversion of the vegetation parameters and has higher precision and quicker computing velocity. This new method doesn't suffer from the limitation of the distribution characteristics of the Pol-InSAR correlation coefficient, so this method could be used to the vegetation parameter inversion by bi- static Pol-InSAR working in the one-transmit bi-receive mode too. Experimental results validate the correctness of the new model and the superiority of this method.In chapter 5, the three-layer vegetation Pol-InSAR correlation coefficient model to bi-static Pol-InSAR system and the vegetation parameter inversion method to it are studied. A new model to the three-layer vegetation complex correlation coefficient of the bi-static Pol-InSAR system is established. Based on this new model, the distribution characteristics of its Pol-InSAR correlation coefficient is analyzed, the limitation of the three-stage inversion method is discussed and a new vegetation parameter inversion method based on the dual-baseline Pol-InSAR is proposed. Three-layer vegetation construction is the extension of the two-layer vegetation construction. Compared to the single-baseline Pol-InSAR, this new method could improve the parameter inversion accuracy and estimate more vegetation parameters. Experimental results validate the correctness of the new model and the superiority of this new method.In chapter 6, a method to improve the moving target detection performance of the fully-polarimetric along-track interferometric SAR system and the optimization design method to the fully-polarimetric along-track interferometric SAR System are studied. To the fully-polarimetric along-track interferometric SAR System working in the pursuit mono-static mode, the concept and the construction method of the polarimetric virtual multi-baseline are proposed. The potential of the moving target detection by the polarimetric virtual multi-baseline is analyzed and advantages of the fully-polarimetric along-track interferometric SAR System to the the single polarimetric along-track interferometric SAR System is compared. An optimization model in which the object function is the proportion of the detectable velocity length of the two interferometric SAR systems is constructed. The variables of the optimization model include the real length of the baseline and the pulse repetition period. The optimization results of the variables maximize the superiority of the polarimetric information in the improvement of the moving target detection performance. To the fully-polarimetric along-track interferometric SAR system working in the alternating bi-static mode, the construction and the characteristics of the polarimetric virtual multi-baseline are studied. The potential of the moving target detection by the polarimetric virtual multi-baseline of this new mode is analyzed. Especially, the potential of the moving target detection by the polarimetric virtual multi-baseline of this new mode is analyzed based on the parameters of the TanDEM-X and the TanDEM-L system.