UAV Image Mosaicing Based Multi-Region Local Projection Deformation

The goal of unmanned aerial vehicle (UAV) image mosaicing is to create natural-looking mosaics free of artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on images. In this paper, we apply local homography to match UAV images, which can reduce misalignment artifacts or “ghosting” in the results compared with 2D projective transforms or global homography. In addition, when an object in three dimensions is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which still causes artifacts near some tall buildings if we only use local homography. We propose a novel stitching method based multi-region local projection deformation, that divides the overlapping regions of input images into several regions, then meshes image to calculate local projections by partitioned regions. Specifically, we use a strategy where multiple regions have different weights for calculating local projections, which can significantly reduce ghosting due to these projections vary with the viewpoint and parallax. The benefits of the proposed approach are demonstrated using a variety of challenging cases.