Vol. 4 No. 1 (2010)
Articles

Unsupervised classification of high resolution satellite imagery by self-organizing neural network

Published October 16, 2010
Árpád Barsi
Katalin Gáspár
Zsuzsanna Szepessy
pdf

APA

Barsi, Árpád, Gáspár, K., & Szepessy, Z. (2010). Unsupervised classification of high resolution satellite imagery by self-organizing neural network. Landscape &Amp; Environment, 4(1), 37–44. Retrieved from https://ojs.lib.unideb.hu/landsenv/article/view/2273

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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