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Articles

Aerial image mosaicking based on the 6-DoF imaging model

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Pages 74-89 | Received 13 Jan 2019, Accepted 29 Jun 2019, Published online: 27 Aug 2019
 

ABSTRACT

Image mosaicking technology is widely used in remote sensing and imaging of unmanned aerial vehicles (UAVs) for its superiority of generating large seamless images with high resolutions. Existing methods generally adopt the 8-DoF (degrees of freedom) homography camera model for image mosaicking, which require large computation cost in parameter optimization and cause distortions in the mosaicked images because of the accumulation errors. In view of this, this paper introduces an efficient mosaicking method based on 6-DoF imaging model to improve the quality of aerial image mosaicking. This derived model reduces the number of unknown variables for parameter optimization, hence improves the time efficiency. Meanwhile, a constraint term is merged into the objective function of optimization by analysing the characteristic of UAV imaging to eliminate the accumulation error in the mosaicking process. The experimental results on multiple datasets demonstrate that the proposed method achieves more accurate mosaicking results efficiently and is robust to different reference images relative to the state-of-the-art method.

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61802294; the Aerospace Science Foundation of China under Grant 20171981008; China Postdoctoral Science Foundation under Grant 2018M633472; and the Fundamental Research Funds for the Central Universities under Grant XJS18034.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant 61802294; the Aerospace Science Foundation of China under Grant 20171981008; China Postdoctoral Science Foundation under Grant 2018M633472; and the Fundamental Research Funds for the Central Universities under Grant XJS18034.

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