DocumentCode :
3334328
Title :
A framework for efficiently parallelizing nonlinear noise reduction algorithm
Author :
Goodenough, David G. ; Han, Tian ; Moa, Belaid ; Lang, Kelsey ; Chen, Hao ; Dhaliwal, Amanpreet ; Richardson, Ashlin
Author_Institution :
Pacific Forestry Centre, Natural Resources Canada, Victoria, BC, Canada
fYear :
2010
fDate :
25-30 July 2010
Firstpage :
2182
Lastpage :
2185
Abstract :
In hyperspectral imagery, noise reduction is a vital and common pre-processing step that needs to be executed accurately and efficiently. Until recently, hyperspectral data was modeled using linear stochastic processes and the noise was assumed to manifest itself in a narrow spatial frequency band. The signal and noise are thus considered independent and most of the proposed noise reduction algorithms transform the hyperspectral data linearly from one space to another for noise and signal separation. Hyperspectral data, however, exhibits nonlinear characteristics making the noise frequency and signal dependent. Therefore, to accurately reduce the noise in hyperspectral data, a nonlinear noise reduction algorithm, such as the one we propose in this paper, must be considered. The algorithm, however, is computationally expensive and requires parallelization. To this end, we offer a framework which we have implemented and evaluated.
Keywords :
image denoising; source separation; stochastic processes; efficiently parallelizing nonlinear noise reduction; hyperspectral data; hyperspectral imagery; linear stochastic process; narrow spatial frequency band; noise frequency; nonlinear characteristics; signal separation; Approximation algorithms; Hyperspectral imaging; Noise reduction; Signal to noise ratio; Time series analysis; Nonlinear noise reduction; analysis; denoising; grid computing; nonlinear; parallelization; time series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Conference_Location :
Honolulu, HI
ISSN :
2153-6996
Print_ISBN :
978-1-4244-9565-8
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2010.5651507
Filename :
5651507
Link To Document :
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