Parallax-tolerant unmanned aerial vehicle remote sensing image stitching based on optimal seams

Low-altitude remote sensing technology using unmanned aerial vehicles (UAVs) has gained wide application. However, compared with satellites, UAVs have a smaller field of view and require image-stitching techniques. Existing stitching algorithms are generally only effective when processing images collected by drones at fixed positions but have poor performance on images collected by drones at different locations. This poses challenges in stitching due to large parallax caused by the movement of foreground objects, resulting in issues such as ghosts and misalignment. To address this, an algorithm based on deep features and optimal seams is proposed for large-parallax UAV remote sensing image stitching. In image registration, the algorithm uses LightGlue to implement feature point extraction and matching. On the basis of accurate image registration, image pre-alignment is achieved using a homography matrix. We construct an energy function using color and structural features and then use dynamic programming to search for the optimal seam in the overlapping area that minimizes the energy function. Finally, we achieve stitching based on the optimal seam. In terms of evaluation metrics, in addition to peak signal-to-noise ratio and structural similarity index measure, we introduce an evaluation metric based on pixel patches for seams, making the quantitative evaluation more objective.