Noise reduction in multispectral images using the self-organizing map

被引：1
|
作者：
Toivanen, P [1 ]
Laukkanen, M [1 ]
Kaarna, A [1 ]
Mielikainen, J [1 ]
机构：
[1] Lappeenranta Univ Technol, Dept Informat Technol, FIN-53851 Lappeenranta, Finland
来源：
ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY VIII | 2002年 / 4725卷
关键词：
multispectral image; noise reduction; nonlinear filtering; noise model; ordering of multivariate data; self organizing maps; machine vision;
D O I：
10.1117/12.478751
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：195 / 201
页数：7
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