Next Article in Journal
Stereoscopic Estimation of Volcanic Ash Cloud-Top Height from Two Geostationary Satellites
Previous Article in Journal
Simulating an Autonomously Operating Low-Cost Static Terrestrial LiDAR for Multitemporal Maize Crop Height Measurements
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 204; doi:10.3390/rs8030204

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

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

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top