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Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)  

Public [article]  inGeocarto international > vol 26 n° 6 (October 2011) . - pp 435 - 457 Titre : Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data Type de document : Article/Communication Auteurs : S. Khorram, Auteur ; H. Yuan, Auteur ; F. Van Der Wiele, Auteur Année de publication : 2011 Article en page(s) : pp 435 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multirésolution [Termes IGN] carte de Kohonen [Termes IGN] classification par réseau neuronal [Termes IGN] données multicapteurs [Termes IGN] fusion d'images [Termes IGN] image Landsat-TM [Termes IGN] image SPOT [Termes IGN] occupation du sol [Termes IGN] Perceptron multicouche [Termes IGN] précision de la classification Résumé : (Auteur) Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat Thematic Mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult. Numéro de notice : A2011-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.600462 Date de publication en ligne : 10/08/2011 En ligne : https://doi.org/10.1080/10106049.2011.600462 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31181 [article]

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