Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery

This paper describes a novel approach of an adaptive fusion method by using Chebyshev polynomial analysis (CPA) for use in remote sensing vegetation imagery. Chebyshev polynomials have been effectively used in image fusion mainly in medium to high noise conditions, though its application is limited...

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Main Authors:	Omar, Zaid, Hamzah, Nur'Aqilah, Stathaki, Tania
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2014
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/51719/ http://dx.doi.org/10.1109/TENCONSpring.2014.6863094
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Institution:	Universiti Teknologi Malaysia

id	my.utm.51719
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spelling	my.utm.517192018-10-14T08:37:17Z http://eprints.utm.my/id/eprint/51719/ Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery Omar, Zaid Hamzah, Nur'Aqilah Stathaki, Tania TK Electrical engineering. Electronics Nuclear engineering This paper describes a novel approach of an adaptive fusion method by using Chebyshev polynomial analysis (CPA) for use in remote sensing vegetation imagery. Chebyshev polynomials have been effectively used in image fusion mainly in medium to high noise conditions, though its application is limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Performance evaluation affirms the approach's ability in reducing computational complexity for remote sensing images affected by noise Institute of Electrical and Electronics Engineers Inc. 2014 Article PeerReviewed Omar, Zaid and Hamzah, Nur'Aqilah and Stathaki, Tania (2014) Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery. IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium . pp. 546-550. http://dx.doi.org/10.1109/TENCONSpring.2014.6863094
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Omar, Zaid Hamzah, Nur'Aqilah Stathaki, Tania Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
description	This paper describes a novel approach of an adaptive fusion method by using Chebyshev polynomial analysis (CPA) for use in remote sensing vegetation imagery. Chebyshev polynomials have been effectively used in image fusion mainly in medium to high noise conditions, though its application is limited to heuristics. In this research, we have proposed a way to adaptively select the optimal CPA parameters according to user specifications. Performance evaluation affirms the approach's ability in reducing computational complexity for remote sensing images affected by noise
format	Article
author	Omar, Zaid Hamzah, Nur'Aqilah Stathaki, Tania
author_facet	Omar, Zaid Hamzah, Nur'Aqilah Stathaki, Tania
author_sort	Omar, Zaid
title	Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
title_short	Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
title_full	Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
title_fullStr	Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
title_full_unstemmed	Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
title_sort	adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2014
url	http://eprints.utm.my/id/eprint/51719/ http://dx.doi.org/10.1109/TENCONSpring.2014.6863094
_version_	1643653053392355328

Adaptive chebyshev polynomial analysis for fusion of remote sensing vegetation imagery

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