Robust Optical and SAR Image Registration Based on OS-SIFT and Cascaded Sample Consensus

Abstract

Although several algorithms have achieved automatic registration on optical and synthetic aperture radar (SAR) images, it is still a challenge to establish enough reliable correspondences between such images due to their different imaging mechanisms. For this purpose, we propose a robust point-feature-based registration method. Considering the inherent properties of optical image and SAR image, two different gradient operators are utilized to construct scale spaces and extract features. A new gradient operator is defined for SAR image, yielding a more consistent gradient with optical image gradient calculated by the multiscale Sobel operator. Then, a novel cascaded matching method called cascaded sample consensus (CSC) is put forward to increase the number of correct correspondences. In the first matching, a simple but effective scale constraint strategy is used to remove outliers for a robust initial transformation model. Considering the spatial location relationship in each correct matching pair, the second matching constructs precise search spaces of the best matching points for more correspondences. Experimental results finally verify the robustness and accuracy of the proposed algorithm.