Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

Remote sensing image fusion based on orientation information in nonsubsampled contourlet transform domain

Authors
Yunxiang Tian, Xiaolin Tian
Corresponding Author
Yunxiang Tian
Available Online November 2016.
DOI
https://doi.org/10.2991/aest-16.2016.7How to use a DOI?
Keywords
image fusion; orientation information; nonsubsampled contourlet transform.
Abstract
For improving the traditional fusion algorithm quality and detail information, a fusion algorithm based on orientation information and pulse coupled neural networks (PCNN) in nonsubsampled contourlet transform (NSCT) domain has been proposed. Firstly, convert the multispectral (MS) image into intensity hue saturation (IHS) colour space. Match the histogram of panchromatic (PAN) image to the histogram of I component of MS image. Then decompose the I component and matched PAN image by NSCT, and apply orientation information combined PCNN fusion rules to NSCT coefficients. Reconstruct the fused I component by inverse NSCT transform. Finally, convert the fused MS image back to RGB space. A large number of experiment results have been done to prove that the method proposed in this paper gives better results than the other techniques used.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
Part of series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-257-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/aest-16.2016.7How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yunxiang Tian
AU  - Xiaolin Tian
PY  - 2016/11
DA  - 2016/11
TI  - Remote sensing image fusion based on orientation information in nonsubsampled contourlet transform domain
BT  - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 57
EP  - 63
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.7
DO  - https://doi.org/10.2991/aest-16.2016.7
ID  - Tian2016/11
ER  -