Wavelet threshold denoising for hyperspectral data in spectral domain

Lili Jiang; Xiaomei Chen; Guoqiang Ni; Shule Ge

doi:10.1117/12.815808

Wavelet threshold denoising for hyperspectral data in spectral domain

Lili Jiang^*, Xiaomei Chen, Guoqiang Ni, Shule Ge

^*Corresponding author for this work

School of Optics and Photonics

Beijing Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.

Original language	English
Article number	728519
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7285
DOIs	https://doi.org/10.1117/12.815808
Publication status	Published - 2008
Event	International Conference on Earth Observation Data Processing and Analysis, ICEODPA - Wuhan, China Duration: 28 Dec 2008 → 30 Dec 2008

Keywords

Hyperspectral data
Spectral domain
Wavelet threshold denoising

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10.1117/12.815808

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title = "Wavelet threshold denoising for hyperspectral data in spectral domain",

abstract = "An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.",

keywords = "Hyperspectral data, Spectral domain, Wavelet threshold denoising",

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year = "2008",

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language = "English",

volume = "7285",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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TY - JOUR

T1 - Wavelet threshold denoising for hyperspectral data in spectral domain

AU - Jiang, Lili

AU - Chen, Xiaomei

AU - Ni, Guoqiang

AU - Ge, Shule

PY - 2008

Y1 - 2008

N2 - An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.

AB - An improved method of wavelet threshold denoising is introduced and applied to hyperspectral imagery denoising in spectral domain. This method estimates a threshold value for each spectrum. Thresholds are set to a scalar specifying the percentage of cumulative power to retain in the filtered wavelet transform. Find the actual percent corresponding to these coefficients. During the processing, four families of mother wavelets (Symlets, Daubechies, Haar and Coiflet) are tested in a series of experiments to estimate the functioning of those wavelets and thresholding parameters. Experimental results show that the proposed algorithm with Coiflet provides an improvement in SNR for hyperspectral data specially.

KW - Hyperspectral data

KW - Spectral domain

KW - Wavelet threshold denoising

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U2 - 10.1117/12.815808

DO - 10.1117/12.815808

M3 - Conference article

AN - SCOPUS:60649084073

SN - 0277-786X

VL - 7285

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

M1 - 728519

T2 - International Conference on Earth Observation Data Processing and Analysis, ICEODPA

Y2 - 28 December 2008 through 30 December 2008

ER -

Wavelet threshold denoising for hyperspectral data in spectral domain

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Keywords

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