A feature matching method based on Log-Gabor filtering is proposed to address the problem of high-precision matc-hing for multimodal remote sensing images.The method adopts a multi-scale dense matching framework via a coarse-to-fine manner,which avoids the low repeatability problem of feature detectors in multimodal images and is able to extract a large number of accurate correspondences.The method consists of two main steps:first,a feature pyramid robust to non-linear ra-diometric differences between images is constructed using multi-scale multi-angle Log-Gabor filters;then,the coarse feature map is used for dense template matching to extract a large number of coarse feature correspondences;the feature pyramid is then used to achieve bottom-up refinement of coarse correspondences layer by layer.Furthermore,to address the problem of inefficient sliding window operation for template matching,a fast implementation method of dense template matching is pro-posed,which effectively reduces the running time of dense template matching.The results show that the proposed method can overcome the influence of non-linear radiation differences between images,and outperforms existing multimodal image feature matching methods in terms of the number of correct matches,matching accuracy and matching precision.
万方数据
维普
