Research Of Key Technology Of InSAR And Multi-baseline InSAR

Interferometry Synthetic Aperture Radar (InSAR) is a novel remote sensing technique that has been developed recently. Due to its capability of all-weather and all-time, and high-efficiently acquiring digital elevation maps (DEMs) of target scene, InSAR has been applied widely in topography mapping, the detection of surface deformations, the detection of target etc. It is well-known that conventional InSAR is sensitive to system noise and atmospheric effect. Multi-baseline InSAR, considered as the extension of conventional InSAR, is able to acquire high-precision DEMs of earth surface and has become an issue of remote sensing technology application research since it can overcome inherent defect of conventional InSAR.The principle and the methods of main steps of InSAR data processing, including SAR image registration, flat earth removal, noise filtering, phase unwrapping etc, are investigated and discussed, and two key steps in InSAR data processing, including noise filtering and phase unwrapping are studied in detail. In addition, multi-baseline phase unwrapping technique, considered as the key technology of multi-baseline InSAR data processing, is also studies in detail.The main work and innovations accomplished in this dissertation are as follows:(1) The techniques of image registration, flat earth removal and noise filtering etc, is investigated and discussed in this dissertation. It is difficult to normal filtering algorithms to remove effectively phase noise in interferogram, and with good edge preservation. To solve above problem, a combined filtering algorithm that can remove phase noise effectively, and with good edge preservation is proposed in this dissertation.(2) Conventional phase unwrapping algorithms are investigated and summarized. As is known it is difficult for conventional algorithms to unwrap effectively the interferogram with complex fringe, or with dense fringe. To solve above problem, a novel phase unwrapping algorithm based on the unscented Kalman filter (UKF) is proposed. This method is the result of combining an UKF with the path-following strategy and an omni-directional local phase slope estimator. Simulation and real data processing results validate the effectiveness of proposed method, and show a significant improvement with respect to some conventional phase unwrapping algorithms in some situations. On this basis, combining an UKF with the artificial-intelligence search strategy,an novel UKF phase unwrapping algorithm based on the artificial-intelligence search strategy is proposed. The artificial-intelligence search strategy will ensure that this algorithm performs noise filtering and phase unwrapping along the path from high-quality region to low-quality region, and will guarantee unwrapped neighbors information are fully exploited. Therefore, phase wrapping accuracy can be improved further.(3) As for the nonlinearity and the non-Gaussian characteristic of the phase unwrapping problem, a novel phase unwrapping algorithm based on the particle filter (PF) is proposed. This method provides independence from noise statistics and is not constrained by the nonlinearity of the problem. Simulation and real data processing results validate the effectiveness of proposed method.(4) Classical multi-baseline phase unwrapping algorithms are investigated and summarized. As is well-known that adaptability and stability of conventional algorithm isn't strong enough. To solve above problem, a novel multi-baseline phase unwrapping algorithm based on the UKF is proposed. The performance of the proposed method from synthetic data is illustrated. In addition, a novel multi-baseline phase unwrapping algorithm is proposed by using the strong data fusion capacity of extended particle filter (EPF), and the effectiveness of proposed method is validated by Simulation data processing results.(5) A novel multi-baseline phase estimate algorithm, applied in SAR interferometry with more than three baselines, is proposed. This method consists of two steps: firstly, selecting the appropriate set of baselines and unwraping the interferogram associated with the shortest baseline; then gaining the unwrapped phase of the longest baseline by using the maximum likelihood estimator to extract the frequency of any complex pixel.