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Open AccessArticle

Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications

1
Research Institute of Unmanned Aerial Vehicle, Beihang University, Beijing 100191, China
2
Asia-Pacific Space Cooperation Organization, Beijing 100191, China
3
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
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Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Remote Sens. 2015, 7(10), 12606-12634; https://doi.org/10.3390/rs71012606
Received: 5 July 2015 / Revised: 15 September 2015 / Accepted: 17 September 2015 / Published: 25 September 2015
Global motion estimation (GME) is a key technology in unmanned aerial vehicle remote sensing (UAVRS). However, when a UAV’s motion and behavior change significantly or the image information is not rich, traditional image-based methods for GME often perform poorly. Introducing bottom metadata can improve precision in a large-scale motion condition and reduce the dependence on unreliable image information. GME is divided into coarse and residual GME through coordinate transformation and based on the study hypotheses. In coarse GME, an auxiliary image is built to convert image matching from a wide baseline condition to a narrow baseline one. In residual GME, a novel information and contrast feature detection algorithm is proposed for big-block matching to maximize the use of reliable image information and ensure that the contents of interest are well estimated. Additionally, an image motion monitor is designed to select the appropriate processing strategy by monitoring the motion scales of translation, rotation, and zoom. A medium-altitude UAV is employed to collect three types of large-scale motion datasets. Peak signal to noise ratio (PSNR) and motion scale are computed. This study’s result is encouraging and applicable to other medium- or high-altitude UAVs with a similar system structure. View Full-Text
Keywords: global motion estimation; unmanned aerial vehicle; metadata; remote sensing global motion estimation; unmanned aerial vehicle; metadata; remote sensing
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Li, H.; Li, X.; Ding, W.; Huang, Y. Metadata-Assisted Global Motion Estimation for Medium-Altitude Unmanned Aerial Vehicle Video Applications. Remote Sens. 2015, 7, 12606-12634.

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