To achieve registration of multi-sensor images by utilizing complementary information, this paper proposes an iterative image registration method based on scale invariant feature transformation (SIFT) and extended phase correlation (EPC), named as SIFT_IEPC. The reference image and the sensed image are pre-registered by SIFT and a geometrical outlier removal method. Overlapping regions corresponding to the reference image and the rectified sensed image are partitioned to block image with equal size, and the extended phase correlation is used to estimate the translation parameters between each block pairs, which are used to tune the matched feature point pairs in the block. The tuned feature point sets are used to update the registration parameters between the reference image and the sensed image. Repeat the process of EPC matching and feature tuning until terminate condition is satisfied. Experiments on three pairs including simulated and real remote sensing images are conducted to evaluate the performance of SIFT_IEPC. The comparison experiments demonstrate that SIFT_IEPC can apparently increase the accuracy of image registration.
The accuracy of two sets of feature points is significant to remote sensing image registration based on feature matching. This paper proposes a novel image registration method based on geometrical outlier removal. The purpose of this algorithm is to eliminate most outliers and preserve as much inliers as possible. We formulate the outlier elimination method into a mathematical model of optimization, the geometric relationship of feature points is the constraint, and derive a simple closed-form solution with linear time and linear space complexities. This algorithm is divided into three key steps. First two remote sensing images are registered by scale-invariant feature transform(SIFT) algorithm. The initial feature points are generated by this step. Then the mathematical model is built and the optimal solution is calculated based on the initial feature points. Last we compare two recent registration results based on the optimal solution, and determine if it is necessary to update the initial feature points and recalculate. The experiment results demonstrate the accuracy and robustness of the proposed algorithm.