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Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

2,†, 1,*,†, 3, 2 and 4
College of Civil Engineering, Hunan University, Changsha 410082, Hunan, China
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, Hunan, China
College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, Hunan, China
School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Pablo J. Zarco-Tejada, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2016, 8(3), 204;
Received: 12 December 2015 / Revised: 4 February 2016 / Accepted: 17 February 2016 / Published: 2 March 2016
PDF [11208 KB, uploaded 2 March 2016]


The mosaicking of Unmanned Aerial Vehicle (UAV) imagery usually requires information from additional sensors, such as Global Position System (GPS) and Inertial Measurement Unit (IMU), to facilitate direct orientation, or 3D reconstruction approaches (e.g., structure-from-motion) to recover the camera poses. In this paper, we propose a novel mosaicking method for UAV imagery in which neither direct nor indirect orientation procedures are required. Inspired by the embedded deformation model, a widely used non-rigid mesh deformation model, we present a novel objective function for image mosaicking. Firstly, we construct a feature correspondence energy term that minimizes the sum of the squared distances between matched feature pairs to align the images geometrically. Secondly, we model a regularization term that constrains the image transformation parameters directly by keeping all transformations as rigid as possible to avoid global distortion in the final mosaic. Experimental results presented herein demonstrate that the accuracy of our method is twice as high as an existing (purely image-based) approach, with the associated benefits of significantly faster processing times and improved robustness with respect to reference image selection. View Full-Text
Keywords: UAV; sequential imagery; image mosaicking; homography energy model UAV; sequential imagery; image mosaicking; homography energy model

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Xu, Y.; Ou, J.; He, H.; Zhang, X.; Mills, J. Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses. Remote Sens. 2016, 8, 204.

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