Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses
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 camer...
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doaj-efe011ce9a7f4219abd12cccc22d62252020-11-25T00:01:33ZengMDPI AGRemote Sensing2072-42922016-03-018320410.3390/rs8030204rs8030204Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera PosesYuhua Xu0Jianliang Ou1Hu He2Xiaohu Zhang3Jon Mills4College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, Hunan, ChinaCollege of Mechanical and Electrical Engineering, Central South University, Changsha 410083, Hunan, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, Hunan, ChinaSchool of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UKThe 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.http://www.mdpi.com/2072-4292/8/3/204UAVsequential imageryimage mosaickinghomography energy model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuhua Xu Jianliang Ou Hu He Xiaohu Zhang Jon Mills |
spellingShingle |
Yuhua Xu Jianliang Ou Hu He Xiaohu Zhang Jon Mills Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses Remote Sensing UAV sequential imagery image mosaicking homography energy model |
author_facet |
Yuhua Xu Jianliang Ou Hu He Xiaohu Zhang Jon Mills |
author_sort |
Yuhua Xu |
title |
Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses |
title_short |
Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses |
title_full |
Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses |
title_fullStr |
Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses |
title_full_unstemmed |
Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses |
title_sort |
mosaicking of unmanned aerial vehicle imagery in the absence of camera poses |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2016-03-01 |
description |
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. |
topic |
UAV sequential imagery image mosaicking homography energy model |
url |
http://www.mdpi.com/2072-4292/8/3/204 |
work_keys_str_mv |
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