Geometrie feature-based image co-registration approach for InSAR

This paper proposes a novel approach to co-register InSAR image pair. We use the SIFT descriptor to extract scale- and rotation-invariant point correspondences from images. Since the images are acquired spatial or temporal differently, there are unavoidable differences between them, which result in some mismatches in the correspondences. Generally, these mismatches must be removed so as to achieve a correct co-registration. However, this step is not necessary in our approach. Base on the obtained correspondences and the geometric transform relationship between images, a series of equations are formulated to calculate the samples of co-registration parameters: the rotation, scale, azimuth translation and range translation. We transform the parameter estimation problem into a linear regression problem, and the robust Least Median of Squares (LMedS) is introduced to obtain the precise value of co-registration parameters from these contaminated samples. Experiments show that the approach is able to co-register images with high precision and robustness even if substantial nonoverlapped areas exist between images.