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

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ID: 194019
2010
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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.
Reference Key
barsi2010actaunsupervised Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;ÁRPÁD BARSI;KATALIN GÁSPÁR;ZSUZSANNA SZEPESSY
Journal acta geographica debrecina: landscape and environment series
Year 2010
DOI
DOI not found
URL
http://landscape.geo.klte.hu/pdf/agd/2010/2010v4is1_4.pdf
Keywords
artificial neural network clustering high resolution imageryenvironmental sciencesecology

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