VSP-Based Warping for Stitching Many UAV Images

Image stitching aims to mosaic individual images together to build a broader panorama, wherein image registration is the most critical step. In this step, all subimages are normally brought into alignment by warping functions, each of which is often parameterized by rotation, scale, and translation factors. Existing methods usually have to estimate such parameters simultaneously, which incur the problems of unstable and time-consuming computations, especially for stitching large number of unmanned aerial vehicle (UAV) images. In this article, a novel stitching method using vector shape preserving (VSP)-based warping is proposed, which is especially suitable for stitching many UAV images. We innovatively construct warping vectors and involve them to measure registration error to preserve vector shapes, which allows separately dealing with a re-plane stage and a translation stage. Additionally, a novel scale regularization processing connected to the warping is designed for tractable computation. Our method is able to achieve low computational cost, high alignment accuracy, and meanwhile valid real-world interpretation. Experimental results on four real-world UAV image datasets validate the excellent performance of our method.