This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect.

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.

The goal of unmanned aerial vehicle(UVA) image mosaicking is to create natural-looking mosaics without 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 the images. Moreover, 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 causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multi-region guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.