Centre de documentation
scientifique

Global elevation ancillary data for land-use classification using granular neural networks / D. Stathakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 1 (January 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 1 (January 2008) . - pp 55 - 63 Titre : Global elevation ancillary data for land-use classification using granular neural networks Type de document : Article/Communication Auteurs : D. Stathakis, Auteur ; I. Kanellopoulos, Auteur Année de publication : 2008 Article en page(s) : pp 55 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] altitude [Termes IGN] classification floue [Termes IGN] classification par réseau neuronal [Termes IGN] données auxiliaires [Termes IGN] fusion d'images [Termes IGN] granularité d'image [Termes IGN] logique floue [Termes IGN] utilisation du sol Résumé : (Auteur) The development of digital global databases containing data such as elevation and soil can greatly simplify and aid in the classification of remotely sensed data to create land-use classes. An efficient method that can simultaneously handle diverse input dimensions can be formed by merging fuzzy logic and neural networks. The so-called granular or fuzzy neural networks are able not only to achieve high classification levels, but at the same time produce compressed and transparent neural network skeletons. Compression results in reduced training times, while transparency is an aid for interpreting the structure of the neural network by translating it into meaningful rules and vice versa. The purpose of this paper is to provide some initial guidelines for the construction of granular neural networks in the remote sensing context, while using global elevation ancillary data within the classification process. Copyright ASPRS Numéro de notice : A2008-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.1.55 En ligne : https://doi.org/10.14358/PERS.74.1.55 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29009 [article]