Abstract
Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.
Highlights
Unmanned aerial vehicle (UAV) remote sensing systems have been widely used in environmental monitoring, ecological farming, disaster emergency management, navigation map production, and 3D urban reconstruction because of its advantages of low cost, fast data collection, and easy operation [1,2,3,4,5].Due to its low flight altitude and the camera perspective constraints, the coverage area of a singleunmanned aerial vehicle (UAV) image is small
Because high-altitude wind has a significant impact on the UAV platform due to its light-weight [6], problems such as irregular image overlapping and uneven image exposure are introduced into the adjacent images
This paper proposes a new algorithm to realize the seamless stitching of UAV images through a comprehensive theoretical analysis of the dynamic programming algorithm
Summary
Unmanned aerial vehicle (UAV) remote sensing systems have been widely used in environmental monitoring, ecological farming, disaster emergency management, navigation map production, and 3D urban reconstruction because of its advantages of low cost, fast data collection, and easy operation [1,2,3,4,5].Due to its low flight altitude and the camera perspective constraints, the coverage area of a singleUAV image is small. Various methods for seamless stitching of UAV remote sensing images have been investigated [7,8,9,10,11,12,13,14,15,16,17] These methods can generally be classified into two types: weighted fusion algorithms and seam line-based algorithms [7,8,9,10,11,12,13,14]. The weighted fusion algorithms focus on overlapping area pixels on the adjacent images They adopted algorithms to eliminate the seams, which can be and implemented and can effectively adjust the exposure difference [7,8,9,10]
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