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A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum

Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation’s biophysical and biochemical characteri...

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Main Authors: Mohd Shafri, Helmi Zulhaidi, Ebadi, Ladan
Format: Article
Language:English
Published: Springer Berlin Heidelberg 2015
Online Access:http://psasir.upm.edu.my/id/eprint/43605/1/A%20stable%20and%20accurate%20wavelet-based%20method%20for%20noise%20reduction%20from%20hyperspectral%20vegetation%20spectrum.pdf
http://psasir.upm.edu.my/id/eprint/43605/
https://www.researchgate.net/publication/271661033_A_stable_and_accurate_wavelet-based_method_for_noise_reduction_from_hyperspectral_vegetation_spectrum
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spelling my.upm.eprints.436052016-07-21T09:26:04Z http://psasir.upm.edu.my/id/eprint/43605/ A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum Mohd Shafri, Helmi Zulhaidi Ebadi, Ladan Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation’s biophysical and biochemical characteristics. Additionally, hyperspectral signals are usually studied using the derivative analysis method that is very sensitive to noise in the data. This study investigates denoising of the hyperspectral vegetation spectrum using different wavelet-based methods. A test signal and several real-world vegetation spectra are denoised using four wavelet methods: traditional discrete wavelet transform (DWT); stationary wavelet transform (SWT); lifting wavelet transform (LWT); and a combination of SWT and LWT, which in this paper is called stationary lifting wavelet transform (SLWT). SLWT incorporates the advantages of both SWT and LWT methods, including a translation invariance property and a fast simple algorithm. Experimental results show that SLWT highly outperforms other wavelet-based methods in terms of accuracy and visual quality. Furthermore, this research reveals the following novel results: SLWT 1) for different levels of decomposition of the wavelet transform gives similar results and its denoising results is independent to the selection of decomposition level; 2) generates stable statistical results; 3) can make use of mother wavelets with small filter size (i.e., low-order mother wavelets) that are suitable for preserving subtle features in vegetation spectrum; and 4) its denoising results do not depend on the selection of the mother wavelet when applying low-order mother wavelets. Springer Berlin Heidelberg 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43605/1/A%20stable%20and%20accurate%20wavelet-based%20method%20for%20noise%20reduction%20from%20hyperspectral%20vegetation%20spectrum.pdf Mohd Shafri, Helmi Zulhaidi and Ebadi, Ladan (2015) A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum. Earth Science Informatics, 8 (2). pp. 411-425. ISSN 1865-0473; ESSN: 1865-0481 https://www.researchgate.net/publication/271661033_A_stable_and_accurate_wavelet-based_method_for_noise_reduction_from_hyperspectral_vegetation_spectrum 10.1007/s12145-014-0168-0
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation’s biophysical and biochemical characteristics. Additionally, hyperspectral signals are usually studied using the derivative analysis method that is very sensitive to noise in the data. This study investigates denoising of the hyperspectral vegetation spectrum using different wavelet-based methods. A test signal and several real-world vegetation spectra are denoised using four wavelet methods: traditional discrete wavelet transform (DWT); stationary wavelet transform (SWT); lifting wavelet transform (LWT); and a combination of SWT and LWT, which in this paper is called stationary lifting wavelet transform (SLWT). SLWT incorporates the advantages of both SWT and LWT methods, including a translation invariance property and a fast simple algorithm. Experimental results show that SLWT highly outperforms other wavelet-based methods in terms of accuracy and visual quality. Furthermore, this research reveals the following novel results: SLWT 1) for different levels of decomposition of the wavelet transform gives similar results and its denoising results is independent to the selection of decomposition level; 2) generates stable statistical results; 3) can make use of mother wavelets with small filter size (i.e., low-order mother wavelets) that are suitable for preserving subtle features in vegetation spectrum; and 4) its denoising results do not depend on the selection of the mother wavelet when applying low-order mother wavelets.
format Article
author Mohd Shafri, Helmi Zulhaidi
Ebadi, Ladan
spellingShingle Mohd Shafri, Helmi Zulhaidi
Ebadi, Ladan
A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
author_facet Mohd Shafri, Helmi Zulhaidi
Ebadi, Ladan
author_sort Mohd Shafri, Helmi Zulhaidi
title A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
title_short A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
title_full A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
title_fullStr A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
title_full_unstemmed A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
title_sort stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum
publisher Springer Berlin Heidelberg
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/43605/1/A%20stable%20and%20accurate%20wavelet-based%20method%20for%20noise%20reduction%20from%20hyperspectral%20vegetation%20spectrum.pdf
http://psasir.upm.edu.my/id/eprint/43605/
https://www.researchgate.net/publication/271661033_A_stable_and_accurate_wavelet-based_method_for_noise_reduction_from_hyperspectral_vegetation_spectrum
_version_ 1643833615064236032
score 13.211869

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