To address the issue of uneven distribution and insufficient correctly matched feature points in weakly-textured aircraft skin, an improved LoFTR algorithm is proposed for stitching aircraft skin images. Based on the posture of the camera, cylindrical back-projection is utilized to correct skin image curvature. By determining the feature extraction area through the overlapping regions between images, the generation of falsely matched point pairs is reduced. The LoFTR algorithm is employed for feature extraction, and the RANSAC algorithm is applied for feature point sorting. Adhering to the idea of image partitioning, grid division is used on overlapping areas for further sorting of feature points, ensuring a more even distribution, thereby yielding a more accurate transformation matrix for image registration. Experiments conducted on aircraft skin images collected via our self-developed unmanned vehicles confirmed the efficacy of this improved method. A feature matching rate comparison experiment with SIFT, SURF, ORB, BRISK, and AKAZE showed match rates of 4.84%, 0.47%, 2.9%, 0.86%, and 5.08%, respectively, while the proposed algorithm achieved a feature match rate of 55.21%. The average SSIM increased by 44.38% to 88.46%. The proposed method is effective for stitching tasks of aircraft skin images, and it eliminates the issue of missed stitches due to weak textures.