分类： 地球科学 >> 空间物理学 提交时间： 2017-01-04
摘要： A fast offset estimation approach for interferometric synthetic aperture radar (InSAR) image pair subpixel registration is proposed for cases of relatively gentle topography and/or short baseline. A coarse-to-fine registration strategy is taken. The pixel-level offset is estimated in the coarse registration step by a fast feature-based estimation, which uses the speeded up robust feature operator and fast least trimmed squares (Fast-LTS) estimator to accelerate the feature extraction and parameter estimation. A fine registration is performed subsequently. The conventional normalized cross-correlation algorithm (NCCA) searches for the optimal subpixel offset by oversampling either the coarse cross correlation or the InSAR image patch pair. The offset estimation accuracy is restricted by the oversampling rate, and the computational burden is heavy when high accuracy is demanded. In this letter, we transform the oversampling and correlation searching process of NCCA into a nonlinear optimization problem, which takes the maximization of the coherent cross correlation as the objective function; by solving it, the subpixel offset can be fast and exactly obtained without any image oversampling. The final registration parameters are inverted by Fast-LTS fitting of a series of subpixel tie point correspondences which can be constructed after applying the approach to several image patch pairs. RadarSat-2 data are used to test the approach, and the results show that it performs very well not only on the speed but also on the accuracy. �2006 IEEE.
分类： 地球科学 >> 空间物理学 提交时间： 2017-01-04
摘要： An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed, the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation, and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for general SAR image registration. �2012 SPIE.
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