Combining Neighborhood Difference and Gaussian-Gamma-Shaped Feature Map for SAR Image Registration

Abstract

Due to the influence of speckle noise and geometric distortion between images, synthetic aperture radar (SAR) image registration under different imaging conditions is a challenging task in remote sensing. To address the issues of significant differences in scattering and geometric characteristics of SAR images under different viewing angles, this letter proposes a novel SAR image registration method. The existing methods mainly rely on gradient information in the feature point selection process, which leads to uneven distribution of feature points and poor global matching. We design a Harris-based neighborhood difference map (HNDM) detector. This detector uses the degree of difference between neighbor regions and the central region to obtain feature points that are homogeneous and significant. Then, a Gaussian-Gamma-shaped (GGS) feature map is used to construct the feature point characterization, which is more robust to dark region noise. Experimental results of SAR image registration under different conditions show that our method achieves better performance in matching accuracy and the number of correct correspondences, outperforming three existing advanced algorithms.