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UAV Image Mosaicking Based on Multi-region Guided Local Projection Deformation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3006289
Quan Xu , Jun Chen , Linbo Luo , Wenping Gong , Yong Wang

The goal of unmanned aerial vehicle (UAV) 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 3-D 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 multiregion 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 2-D 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.

中文翻译:

基于多区域引导局部投影变形的无人机图像拼接

无人机 (UAV) 图像拼接的目标是创建看起来自然的拼接图,而不会因图像的视差和相对的相机运动而产生伪影。无人机遥感是一种低空技术,无人机成像的场景不是有效的平面,在图像上产生视差。此外,当 3-D 对象映射到图像平面时,不同的表面具有不同的投影。这些投影随无人机图像序列中的视点而变化,这会导致拼接图像中某些高楼附近的伪影。为了解决这些问题,我们提出了一种基于多区域引导局部投影变形的新拼接方法,可以显着减少由于这些投影随视点和视差而变化的重影。在提出的方法中,图像最初被网格化,每个单元对应于图像匹配的局部单应性,与二维投影变换或全局单应性相比,这可以减少结果中的错位伪影。然后,我们通过对特征点进行分类,将输入图像的重叠区域划分为多个区域。服务于良好场景约束的分区区域被用来指导局部单应性的计算。具体来说,不是通过图像中所有特征点与网格顶点之间的距离来计算局部单应性,我们提出了一种多个区域具有不同权重的策略来计算局部单应性,这可以显着减少一些高楼附近的重影。使用各种具有挑战性的案例证明了所提出方法的好处。
更新日期:2020-01-01
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