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Mapping land cover from detailed aerial photography data using textural and neural network analysis / R. Cots-Folch in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°7-8 (April 2007) . - pp 1625 - 1642 Titre : Mapping land cover from detailed aerial photography data using textural and neural network analysis Type de document : Article/Communication Auteurs : R. Cots-Folch, Auteur ; M.J. Aitkenhead, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1625 - 1642 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal [Termes IGN] image aérienne [Termes IGN] paysage agricole [Termes IGN] photographie panchromatique [Termes IGN] utilisation du sol Résumé : (Auteur) Automated mapping of land cover using black and white aerial photographs, as an alternative method to traditional photo-interpretation, requires using methods other than spectral analysis classification. To this end, textural measurements have been shown to be useful indicators of land cover. In this work, a neural network model is proposed and tested to map historical land use/land cover (LUC) from very detailed panchromatic aerial photographs (5m resolution) using textural measurements. The method is used to identify different land use and management types (e.g. traditional versus mechanized vineyard systems). These have been tested with known ground reference data. The results show the potential of the methodology to obtain automatic, historic, and very detailed cartography information from a complex landscape such as the mountainous and Mediterranean region to which it is applied here, and the advantages that this method has over traditional methods. Copyright Taylor & Francis Numéro de notice : A2007-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600887722 En ligne : https://doi.org/10.1080/01431160600887722 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28540 [article]

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