UAV image matching of mountainous terrain using the LoFTR deep learning model

In natural terrain scene UAV image matching, traditional feature point-based methods often have problems such as an unstable number of extracted feature points, difficulty in detecting feature points in weak texture areas, uneven distribution, and low robustness. Deep learning-based image matching methods can produce larger and more reasonably distributed matching pairs, so this research paper tries to perform UAV image matching based on a deep learning LoFTR algorithm for natural terrain scenes. The critical technical process was: first, the LoFTR algorithm was used to generate dense feature matching, and then the epipolar line constraints were used to purify the interior points, specifically, this study used the MAGSAC++ method to estimate the fundamental matrix, eliminate the wrong matching pairs, and finally get reliable matching results. In this research paper, six sets of visible images taken by different UAVs equipped with different sensors in the field were selected as experimental data to test the method and were compared and analyzed with the traditional classical SIFT, ASIFT, and AKAZE algorithms and the KeyNet-AdaLAM deep learning method. The experimental results show that the method in this study obtains a dense number of robust matching pairs with uniform spatial distribution in the UAV image matching of natural scenes mainly in mountainous areas, and the comprehensive performance is higher and more advantageous than the comparison methods.