Study On The Applications Of Partial Differential Equations In SAR Interferometry

Synthetic aperture radar (SAR) interferometry is an established technique based on combing two SAR images of the same scene acquired from slightly different viewpoints. It is widely used for topographic mapping and surface change detection, moreover, in the past few years, SAR interferometry has also been used to retrieve physical parameters of terrestrial surfaces. The thesis demonstrate the signal processing of interferometric synthetic aperture radar, especially in the challenging problems such as noise suppressing and two-dimensional phase unwrapping. This paper deeply researched the InSAR signal processing based on partial differential equation (PDE).First, the application of average filter and median filter in the phase denoise is canvassed and the defect of the above filters is pointed out after investigating statistical and distributing property of the phase noise. The paper focus on the noise suppressing technique based on partial differential equation (PDE). One of the difficulties in phase noise filtering is how to remove the noise and preserve the sharp sawtooth profile of the fringe effectively. Noise suppressing based on PDEs is an adaptive method. During the course of noise suppressing, image features and their directions are extracted. Less smoothes in the locations with strong image feature, and more smoothes in the locations with weak image feature; minimal smoothes in the directions across the image features, and maximal smoothes in the directions along the image features. The smoothing can make noises removed while preserve edge well.Two-dimensional phase unwrapping technology, as an exciting field, plays a core role in the application of SAR interferometry. The in-depth research on the theory and technique of phase unwrapping will contributea lot to the InSAR application. The author digs into various least-square unwrapping algorithms , gives a thoroughly compare through analyses and experiments and demonstrates that both the weighted least-square algorithm and the unweighted least-square algorithm can be convert to solving a partial differential equation (PDE) with Neumann boundary. Each algorithm has its own unique suitability to different data, and the choice of the algorithms is depended on the data.