Drone Image Stitching Based on Compactly Supported Radial Basis Function

Image stitching stitches multiple overlapping images into a seamless image according to the corresponding geometric relationship between the reference and source images. In this article, a parallax-tolerant image stitching method based on robust elastic warping is integrated in the stitching of drone images, and locality preserving feature matching is used to effectively remove outliers from a set of putative feature correspondences. Our method can avoid the ghost issue in traditional methods when the scene suffers large view point changes. The method can be divided into three stages, namely, locality preserving feature matching, robust elastic warping, and global projectivity preservation. First, a set of high-precision matched feature points are provided for a pair of drone images, where the locality preserving matching is used. Second, the robust elastic warping function eliminates the parallax error, and the input image is distorted according to the calculated deformation on the nongrid plane. Finally, the global projectivity preserving method is applied to obtain high-precision resultant panoramas. Experiments on several sets of drone images demonstrate that our method can generate better panoramas over the state-of-the-art competitors.