An Improved Algorithm with Superpoint+Superglue Network for UAV Remote Sensing Image Registration

UAV remote sensing image registration has a wide range of applications in the fields of fine geographic information extraction, environmental change monitoring, agriculture and forestry, serving as a fundamental and critical step in supporting various applications. However, existing registration methods often extract fewer feature points, uneven distribution and poor stability when faced with significant changes in land features. Accordingly, in this paper, we propose a UAV remote sensing image registration method based on an improved SuperPoint+SuperGlue deep learning network. We add a feature point extraction branch to the shallow feature map in the SuperPoint network, and add the newly extracted feature points to the existing feature points, and then perform non-maximum suppression (NMS) for further refinement. This enhances its capability to extract image feature points, resulting in more accurate and evenly distributed positions of these points. The experiments carried on two sets of real UAV datasets indicate that our method outperforms several typical registration approaches in terms of registration accuracy and detection efficiency.