Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with r...Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with regard to feature-based remote sensing image registration.There are two key contributions:1)we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2)we design a gradually decreasing resolution of multi-scale pyramid space.And a binary code string is served as feature descriptors to improve matching efficiency.Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed.The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio,accuracy and computation efficiency.展开更多
基金supported by National Nature Science Foundation of China(Nos.61640412 and 61762052)the Natural Science Foundation of Jiangxi Province(No.20192BAB207021)the Science and Technology Research Projects of Jiangxi Province Education Department(Nos.GJJ170633 and GJJ170632).
文摘Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with regard to feature-based remote sensing image registration.There are two key contributions:1)we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2)we design a gradually decreasing resolution of multi-scale pyramid space.And a binary code string is served as feature descriptors to improve matching efficiency.Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed.The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio,accuracy and computation efficiency.
