USuperGlue: an unsupervised UAV image matching network based on local self-attention

Abstract

This study addresses the issues of the limited or unavailable unmanned aerial vehicle (UAV) image matching labels, differences in imagery between UAV and ground cameras, and the limited UAV airborne computer memory in UAV visual navigation. To tackle these challenges, a novel unsupervised UAV image matching network called USuperGlue is developed, which leverages local self-attention. The aim is to enhance the precision and recall of the matching process. To achieve this, a local self-attention module is designed to specialize each feature point at the end of the encoding, thereby further improving the matching precision. Subsequently, the encoded features are decoded to extract scores, keypoints, and feature descriptors. During the feature matching stage, an unsupervised matching network is constructed using SuperGlue, a graph neural network that incorporates multi-head attention, to match the feature descriptors of image pairs. The experiment conducted training of the unsupervised network using the Drone dataset and COCO dataset. The simulation results demonstrate that the trained USuperGlue model achieves superior precision and recall, making it a highly effective solution for UAV image matching.