Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

Geometric Correction Algorithm for UAV Remote Sensing Image Based on Neural Network

Authors
Lirong Diao, Riuan Liu, Tingting Chen
Corresponding Author
Lirong Diao
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.11How to use a DOI?
Keywords
UAV remote sensing image; Neural network; Geometric correction.
Abstract

In the process of the UAV (Unmanned Aerial Vehicle) remote sensing image geometric correction, the method of the geometric correction plays a vital role. Since the neural network is a distributed and parallel mathematical model, and it has good learning ability for nonlinear, so the nonlinear and uncertainty of the UVA remote sensing image geometric correction can be solved well. This paper focuses on the application of BP neural network and RBF neural network in UAV remote sensing image geometric correction, and finally compares the effect of the geometric correction based on BP neural network and RBF neural network through the experiments.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.11How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lirong Diao
AU  - Riuan Liu
AU  - Tingting Chen
PY  - 2016/11
DA  - 2016/11
TI  - Geometric Correction Algorithm for UAV Remote Sensing Image Based on Neural Network
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 59
EP  - 63
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.11
DO  - https://doi.org/10.2991/aiea-16.2016.11
ID  - Diao2016/11
ER  -