OS-Flow: A Robust Algorithm for Dense Optical and SAR Image Registration

Abstract

Coregistration of high-resolution optical and synthetic aperture radar (SAR) images is still an ongoing problem due to different imaging mechanisms of two kinds of remote sensing images. In this paper, we propose an optical flow-based algorithm to solve the dense registration problem [optical-to-SAR (OS)-flow]. Unlike parametric registration methods that estimate a transformation model, OS-flow aims to find pixelwise correspondences between optical and SAR images. Specifically, two frameworks of OS-flow, a global method and a local method, are proposed. Due to the drastic differences between SAR and optical images, two dense feature descriptors, rather than the raw intensities, are utilized to retain the constancy assumption in optical flow estimation. Considering the inherent properties of the two images, two dense descriptors are constructed using consistent gradient computation. After satisfying the constancy assumption, the global method estimates the flow map by optimizing an objective function, and the local method iteratively estimates the flow vector in a local neighborhood. Both methods use the coarse-to-fine matching strategy to address large displacements and reduce the computational cost. Experiments on several optical-to-SAR image pairs in various scenarios show that the proposed methods have a strong ability to match across optical and SAR images and outperform other state-of-the-art methods in terms of registration accuracy.