Seam-Adaptive Structure-Preserving Image Stitching for Drone Images

Drones have been widely used for remote sensing applications. To perform high-quality drone image stitching, this article first proposes a local and global structure-preserving alignment (LGSPA) method that aligns drone images from local dual feature-based and global pixel-based alignment perspectives, while maintaining local linear and global collinear image structures. To enable an optimal image stitching performance, we then propose a seam-adaptive weighting (SAW) scheme to enhance the local alignment accuracy under the guidance of a seam prior. On the ground of LGSPA and SAW, we further develop a seam-adaptive structure-preserving (SASP) image stitching framework to generate the final stitched drone images. Both qualitative and quantitative experimental results demonstrate that LGSPA and SASP are capable of generating higher quality alignment and stitching results than several state-of-the-art methods over multiple challenging aerial scenarios, including low textures, repetitive textures, large parallax, wide baseline, and occlusions.