%0 Journal Article
%T UNSUPERVISED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGERY BY SELF-ORGANIZING NEURAL NETWORK
%A ¨¢RP¨¢D BARSI
%A KATALIN G¨¢SP¨¢R
%A ZSUZSANNA SZEPESSY
%J Acta Geographica Debrecina. Landscape and Environment Series
%D 2010
%I University of Debrecen
%X 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.
%K artificial neural network
%K clustering
%K high resolution imagery
%U http://landscape.geo.klte.hu/pdf/agd/2010/2010v4is1_4.pdf