基于局部拟合一致性的机载SAR图像与仿真SAR图像匹配方法OA

Variations in imaging geometry are the main cause of relative feature distortion in Synthetic Aperture Radar(SAR)images,greatly increasing the difficulty of image matching.Using simulated SAR images as references can remove the feature distortions caused by geometric differences.However,significant differences in scattering characteristics and noise patterns between measured and simulated images still exist.Additionally,since most existing matching algorithms mainly rely on symmetric keypoint detection and descriptor matching,the number and precision of matched points are not optimal.To solve these problems,this paper introduces an asymmetric Local Fitting Consistency(LFC)similarity metric based on the local statistical features of both measured and simulated SAR images.Using this metric,a coarse-to-fine matching framework for airborne and simulated SAR images is designed.Furthermore,terrain features are added to improve keypoint detection diversity,leading to more robust matching between airborne and simulated SAR images.Experimental results show that the proposed LFC-based matching method offers better robustness and accuracy compared to other approaches,significantly surpassing current state-of-the-art algorithms in terms of matching precision and other key metrics.