Centre de documentation
scientifique

Improving land-cover classification using recognition threshold neural networks / M.J. Aitkenhead in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 4 (April 2007) . - pp 413 - 421 Titre : Improving land-cover classification using recognition threshold neural networks Type de document : Article/Communication Auteurs : M.J. Aitkenhead, Auteur ; R. Dyer, Auteur Année de publication : 2007 Article en page(s) : pp 413 - 421 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] image Landsat [Termes IGN] Philippines [Termes IGN] seuillage d'image [Termes IGN] surface cultivée Résumé : (Auteur) The use of neural networks to classify land-cover from remote sensing imagery relies on the ability to determine a winner from the candidate land-cover types based on the imagery information available. In the case of a “winner- takes-all” scenario, this does not allow us a measure of how much the prediction of each pixel’s land-cover can be trusted. We present a three-stage method where only winning candidates which are given a clear lead over the other land-cover types are accepted, with a neighborhood relationship and the application of mixed pixels being used to provide full classification. This method allows us to place more faith in the resulting map than simply taking the winner, and results in a higher accuracy of classification. The method is applied to Landsat imagery of an area of the Philippines where natural, urban, and cultivated land-cover types exist. Copyright ASPRS Numéro de notice : A2007-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.4.413 En ligne : https://doi.org/10.14358/PERS.73.4.413 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28506 [article]