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UNSUPERVISED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY BY SELF-ORGANIZING NEURAL NETWORK

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Author(s): ÁRPÁD BARSI | KATALIN GÁSPÁR | ZSUZSANNA SZEPESSY

Journal: Acta Geographica Debrecina. Landscape and Environment Series
ISSN 1789-4921

Volume: 4;
Issue: 1;
Start page: 37;
Date: 2010;
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Keywords: artificial neural network | clustering | high resolution imagery

ABSTRACT
The current paper discusses the importance of the modern high resolution satellite imagery. The acquired high amount of data must be processed by an efficient way, where the used Kohonen-type self-organizing map has been proven as a suitable tool. The paper gives an introduction to this interesting method. The tests have shown that the multispectral image information can be taken after a resampling step as neural network inputs, and then the derived network weights are able to evaluate the whole image with acceptable thematic accuracy.
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