Title: An efficient stitching algorithm for aerial images with low-overlap

Authors: Qingshan Tang; Huang Jiang; Sijie Li

Addresses: Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, 410114, China ' Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, 410114, China ' Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, 410114, China

Abstract: In fields such as military reconnaissance, the overlap between images captured by UAVs is limited to 15%-30%. To achieve larger perspective panoramic images from low-overlap aerial images, this study proposes an efficient algorithm for image stitching. Specifically, the algorithm utilises the oriented fast and rotated brief (ORB) and grid-based motion statistics (GMS) algorithms. Next, image alignment is achieved by calculating location-dependent homographies based on the grid. For seamless integration, the algorithm combines optimal seam blending and gradient-domain fusion techniques. Experimental results demonstrate that the proposed algorithm outperforms the scale-invariant feature transform (SIFT) and the affine-scale invariant feature transform (ASIFT) algorithms in terms of feature point matching accuracy. Moreover, comparisons with other algorithms, such as the as-projective-as-possible (APAP), adaptive as-natural-as-possible (AANAP), and single-perspective-warps (SPW), it is proved that the proposed algorithm can obtain high-quality stitched images. The problems of slow stitching speed, poor alignment, and ghosting are effectively solved.

Keywords: aerial image; image stitching; motion grid statistics; optimal stitching; gradient fusion.

DOI: 10.1504/IJCSE.2024.142835

International Journal of Computational Science and Engineering, 2024 Vol.27 No.6, pp.654 - 662

Received: 11 May 2023
Accepted: 20 Aug 2023
Published online: 28 Nov 2024 *

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