CFOG-like image registration algorithm based on 3D-structural feature descriptor for suburban optical and SAR

Abstract

Owing to nonlinear radiation differences between optical and synthetic aperture radar (SAR) images, and the inevitable SAR speckle noise, the registration of optical and SAR images is challenging work. This paper proposes a novel pixel-wise 3D-structural feature descriptor based on channel-features-of-oriented-gradients (CFOG), thus, it inherits the fast and robust advantages of CFOG. First, the Block-Harris and grid points feature extraction method is used to obtain sufficient uniformly distributed keypoints from the reference images. Then, the new feature descriptor based on the combination of first-order oriented gradients and second-order gradients (FOSG) is proposed. Specifically, two different operators, the Sobel and the ratio of exponentially weighted averages are utilized to calculate the gradient for the optical and SAR images, respectively. On this basis, the first-order oriented gradients and second-order gradients are calculated by a 3D-Gaussian filter and then normalized along the Z-direction to form the FOSG by superposition. Finally, template matching based on the sum of squared differences similarity metric is implemented in the frequency domain. Our method is experimentally compared with five state-of-the-art methods using six pairs of suburban optical and SAR images. The results show that the proposed algorithm is robust to nonlinear radiation differences and SAR speckle noise, and achieves high accuracy.