OS-SIFT: A Robust SIFT-Like Algorithm for High-Resolution Optical-to-SAR Image Registration in Suburban Areas

Abstract

Although the scale-invariant feature transform (SIFT) algorithm has been successfully applied to both optical image registration and synthetic aperture radar (SAR) image registration, SIFT-like algorithms have failed to register high-resolution (HR) optical and SAR images due to large geometric differences and intensity differences. In this paper, to perform optical-to-SAR (OS) image registration, we proposed an advanced SIFT-like algorithm (OS-SIFT) that consists of three main modules: keypoint detection in two Harris scale spaces, orientation assignment and descriptor extraction, and keypoint matching. Considering the inherent properties of SAR images and optical images, the multiscale ratio of exponentially weighted averages and multiscale Sobel operators are used to calculate consistent gradients for the SAR images and optical images on the basis of which, as a result, two Harris scale spaces can be constructed. Keypoints are detected by finding the local maxima in the scale space followed by a localization refinement method based on the spatial relationship of the keypoints. Moreover, gradient location orientation histogram-like descriptors are extracted using multiple image patches to increase the distinctiveness. The experimental results on simulated images and several HR satellite images show that the proposed OS-SIFT algorithm gives a robust registration result for optical-to-SAR images and outperforms other state-of-the-art algorithms in terms of registration accuracy.