Abstract
This paper introduces an Unmanned Aerial Vehicle (UAV) image stitching method, based on the optimal seam algorithm and half-projective warp, that can effectively retain the original information of the image and obtain the ideal stitching effect. The existing seam stitching algorithms can eliminate the ghosting and blurring problems on the stitched images, but the deformation and angle distortion caused by image registration will remain in the stitching results. To overcome this situation, we propose a stitching strategy based on optimal seam and half-projective warp. Firstly, we define a new difference matrix in the overlapping region of the aligned image, which includes the color, structural and line difference information. Then, we constrain the search range of the seam by the minimum energy, and propose a seam search algorithm based on the global minimum energy to obtain the seam. Finally, combined with the seam position and half-projective warp, the shape of the stitched image is rectified to keep more regions in their original shape. The experimental results of several groups of UAV images show that our method has a superior stitching effect.
Highlights
With the development of Unmanned Aerial Vehicle (UAV) remote sensing technology, its research has been extensively used in urban building planning [1], resources and environment detection [2,3]and other fields
We propose an image stitching strategy based on the optimal seam algorithm and half-projective warp to solve the image stitching task of UAV with parallax
We introduce the experiment of our method on UAV images, and compare it with the existing alignment algorithm and optimal seam algorithm
Summary
With the development of UAV remote sensing technology, its research has been extensively used in urban building planning [1], resources and environment detection [2,3]and other fields. UAV remote sensing has the characteristics of high image resolution, low cost and strong flexibility. It is suitable for collecting low-altitude, high-resolution remote sensing images [4]. In addition to obtaining common RGB images, UAV image remote sensing can obtain hyperspectral images. Hyperspectral images can provide more spectral information than RGB images [5]. Due to the limitation of flight altitude, it is difficult for UAV remote sensing to obtain large-area observation images [6]. It is necessary to stitch the obtained remote sensing images to improve the information acquisition ability of remote sensing images [7,8]
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