Unsupervised classification of high resolution satellite imagery by self-organizing neural network
| dc.contributor.author | Barsi, Árpád | |
| dc.contributor.author | Gáspár, Katalin | |
| dc.contributor.author | Szepessy, Zsuzsanna | |
| dc.date.accessioned | 2021-06-28T11:08:19Z | |
| dc.date.available | 2021-06-28T11:08:19Z | |
| dc.date.issued | 2010-10-16 | |
| dc.description.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. | en |
| dc.format | application/pdf | |
| dc.identifier.citation | Acta Geographica Debrecina Landscape & Environment series, Vol. 4 No. 1 (2010) , 37-44 | |
| dc.identifier.eissn | 1789-7556 | |
| dc.identifier.issn | 1789-4921 | |
| dc.identifier.issue | 1 | |
| dc.identifier.jatitle | Landsc. environ. | |
| dc.identifier.jtitle | Acta Geographica Debrecina Landscape & Environment series | |
| dc.identifier.uri | https://hdl.handle.net/2437/317393 | en |
| dc.identifier.volume | 4 | |
| dc.language | en | |
| dc.relation | https://ojs.lib.unideb.hu/landsenv/article/view/2273 | |
| dc.rights.access | Open Access | |
| dc.subject | artificial neural network | en |
| dc.subject | clustering | en |
| dc.subject | high resolution imagery | en |
| dc.title | Unsupervised classification of high resolution satellite imagery by self-organizing neural network | en |
| dc.type | folyóiratcikk | hu |
| dc.type | article | en |
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