Noise reduction in multispectral images using the self-organizing map

Pekka J. Toivanen; Mikko Laukkanen; Arto Kaarna; Jarno S. Mielikainen

doi:10.1117/12.478751

2 August 2002 Noise reduction in multispectral images using the self-organizing map

Pekka J. Toivanen, Mikko Laukkanen, Arto Kaarna, Jarno S. Mielikainen

Proceedings Volume 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII; (2002) https://doi.org/10.1117/12.478751
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In this paper, a new group of noise reduction methods for multispectral images is presented. First, a 1-dimensional Self-Organizing Map (SOM) is taught using the pixel vectors of the noisy multispectral image. Then, a gray-level index image is formed containing the indexes of the SOM vectors. Several gray-level noise reduction methods are applied to the index image for three noise types: impulse, Gaussian, and coherent noise. Tests are made for three kinds of noise distrubutions: for all channels, for channels 30-50, and for 9 selected channels. Error measures imply that the obtained results are very good for coherent noise images, but rather poor for other noise categories, compared to the bandwise coherent filter.

Citation Download Citation

Pekka J. Toivanen, Mikko Laukkanen, Arto Kaarna, and Jarno S. Mielikainen "Noise reduction in multispectral images using the self-organizing map", Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); https://doi.org/10.1117/12.478751

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