Registration of visible and near infrared unmanned aerial vehicle images based on Fourier-Mellin transform

Registration of visible and near infrared unmanned aerial vehicle images based on Fourier-Mellin... The combination of aerial images acquired in the visible and near infrared spectral ranges is particularly relevant for agricultural and environmental survey. In unmanned aerial vehicle imagery, such a combination can be achieved using a set of several embedded cameras mounted close to each other, followed by an image registration step. However, due to the different nature of source images, usual registration techniques based on feature point matching are limited when dealing with blended vegetation and bare soil patterns. Here, another approach is proposed based on image spatial frequency analysis. This approach, which relies on the Fourier-Mellin transform, has been adapted to homographic registration and distortion issues. It has been successfully tested on various aerial image sets, and has proved to be particularly robust and accurate, providing a registration error below 0.3 pixels in most cases. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Registration of visible and near infrared unmanned aerial vehicle images based on Fourier-Mellin transform

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-016-9437-x
Publisher site
See Article on Publisher Site

Abstract

The combination of aerial images acquired in the visible and near infrared spectral ranges is particularly relevant for agricultural and environmental survey. In unmanned aerial vehicle imagery, such a combination can be achieved using a set of several embedded cameras mounted close to each other, followed by an image registration step. However, due to the different nature of source images, usual registration techniques based on feature point matching are limited when dealing with blended vegetation and bare soil patterns. Here, another approach is proposed based on image spatial frequency analysis. This approach, which relies on the Fourier-Mellin transform, has been adapted to homographic registration and distortion issues. It has been successfully tested on various aerial image sets, and has proved to be particularly robust and accurate, providing a registration error below 0.3 pixels in most cases.

Journal

Precision AgricultureSpringer Journals

Published: Jan 30, 2016

References

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