Unsupervised classification of high resolution satellite imagery by self-organizing neural network
| dc.creator | Barsi, Árpád | |
| dc.creator | Gáspár, Katalin | |
| dc.creator | Szepessy, Zsuzsanna | |
| dc.date | 2010-10-16 | |
| dc.date.accessioned | 2021-06-28T11:08:19Z | |
| dc.date.available | 2021-06-28T11:08:19Z | |
| dc.description | 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. | |
| dc.format | application/pdf | |
| dc.identifier | https://ojs.lib.unideb.hu/landsenv/article/view/2273 | |
| dc.identifier.uri | http://hdl.handle.net/2437/317393 | |
| dc.language | eng | |
| dc.publisher | University of Debrecen, Institute of Earth Sciences | |
| dc.relation | https://ojs.lib.unideb.hu/landsenv/article/view/2273/2148 | |
| dc.source | Landscape & Environment; Vol. 4 No. 1 (2010); 37-44 | |
| dc.source | 1789-7556 | |
| dc.source | 1789-4921 | |
| dc.subject | artificial neural network | |
| dc.subject | clustering | |
| dc.subject | high resolution imagery | |
| dc.title | Unsupervised classification of high resolution satellite imagery by self-organizing neural network | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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