Bidirectional remote sensing image registration method integrating multi-level features and cross-spatial attention
Aiming at the problems that remote sensing image features are difficult to extract and the existing image registration framework has low registration accuracy and efficiency,a bidirectional remote sensing image registration method that combines multi-order features and cross-spatial attention is proposed.First,cross-spatial attention is de-signed to retain multi-scale accurate spatial structure information into channels,and embed it into efficient network blocks to focus on capturing the key information of the image.Secondly,a multi-order feature adaptive fusion module is proposed to be used in feature extraction to adaptively fuse low-order and high-order features to improve the accura-cy of registration.Finally,an enhanced feature matching method is designed to analyze the similarity of features more accurately,establish a two-way matching relationship,and use secondary affine transformation to improve the accuracy and reliability of registration.This method achieved 94.0%correct keypoint probability(PCK)on the Aerial Image data set when α=0.05(α:normalized distance threshold),and the average registration time reached 0.93 seconds.The results show that this method significantly improves the registration accuracy and efficiency of multi-source hetero-geneous remote sensing images.
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