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Proceedings Paper

Noise reduction methods for hyperspectral images
Author(s): Pekka J. Toivanen; Arto Kaarna; Jarno S. Mielikainen; Mikko Laukkanen
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Paper Abstract

Methods for noise reduction in multicomponent spectral images are developed and discussed. Multicomponent spectral images can be corrupted by noise either on all the channels or on some of the channels only. In the first case there are two possibilities: either the noise is on all the channels in the same way or the noise is randomly distributed on all the channels. We studied two methods for noise reduction directly on the multicomponent spectral image: the vector median filter and our new method, the spectrum smoothing, which does not care about neighbouring pixels but tries to reduce noise on one pixel at a time. The idea behind spectrum smoothing lies on the nature of a color spectrum. Color spectrum is naturally smooth, and does not have any peaks, unlike a noisy spectrum would have. If some of the channels are noisy, there is a problem of finding the noisy channels. We came into a conclusion that if a channel correlates poorly with the neighboring channel, the channel can be considered noisy, and filtering is applied to that channel. Results from our new spectrum smoothing filter were very promising for Gaussian noise compared to Gaussian 3 by 3 filter and mean 5 by 5 filter.

Paper Details

Date Published: 13 March 2003
PDF: 7 pages
Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); doi: 10.1117/12.463167
Show Author Affiliations
Pekka J. Toivanen, Lappeenranta Univ. of Technology (Finland)
Arto Kaarna, Lappeenranta Univ. of Technology (Finland)
Jarno S. Mielikainen, Lappeenranta Univ. of Technology (Finland)
Mikko Laukkanen, Lappeenranta Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 4885:
Image and Signal Processing for Remote Sensing VIII
Sebastiano B. Serpico, Editor(s)

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