Abstract
The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.
Highlights
The development of Unmanned Aerial Vehicle (UAV) conforms to the current increasing demand for low-altitude very high resolution (VHR) remote sensing data [1,2,3]
An efficient seam elimination method for UAV images based on Wallis dodging
An efficient seam elimination method for UAV images based on Wallis dodging and and Gaussian distance weight enhancement was proposed
Summary
The development of UAVs conforms to the current increasing demand for low-altitude very high resolution (VHR) remote sensing data [1,2,3]. The mosaic seams mainly come from two sources: (1) the color or brightness differences due to the exposure variation; and (2) the texture misplacement due to geometric deformity, projection differences caused by tall landscapes and image capture position differences [8]. These two types of seams clearly appear on the UAV remote sensing platform, the effective and efficient removal of these seams is essential for the application of UAVs
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