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Artificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n° 11 (June 2006) . - pp 2177 - 2195 Titre : Artificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery Type de document : Article/Communication Auteurs : H. Mills, Auteur ; M.E. Cutler, Auteur ; David Fairbairn, Auteur Année de publication : 2006 Article en page(s) : pp 2177 - 2195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal [Termes IGN] données de terrain [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] montagne [Termes IGN] Perceptron multicouche [Termes IGN] Royaume-Uni Résumé : (Auteur) Upland vegetation represents an important resource that requires frequent monitoring. However, the heterogeneous nature of upland vegetation and lack of ground data require classification techniques that have a high degree of generalization ability. This study investigates the use of artificial neural networks as a means of mapping upland vegetation from remotely sensed data. First, the optimum size of support to map upland vegetation was estimated as being less than 4 m, which suggested that soft classification techniques and high spatial resolution IKONOS imagery were required. The use of high spatial resolution imagery for regional-scale areas has introduced new challenges to the remote sensing community, such as using limited ground data and mapping land-cover dynamics and variation over large areas. This work then investigated the utility of artificial neural networks (ANN) for regional-scale upland vegetation from IKONOS imagery using limited ground data and to map unseen data from remote geographical locations. A Multiple Layer Perceptron was trained with pixels from an IKONOS image using early stopping; however, despite high classification accuracies when calculated for pixels from an area where training pixels were extracted, the networks did not produce high accuracies when applied to unseen data from a remote area. Copyright Taylor & Francis. Numéro de notice : A2006-299 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500396501 En ligne : https://doi.org/10.1080/01431160500396501 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28026 [article]

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