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Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

Public [article]  inGeocarto international > vol 28 n° 1-2 (February - May 2013) . - pp 114 - 129 Titre : Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region Type de document : Article/Communication Auteurs : George P. Petropoulos, Auteur ; Krishna Prasad Vadrevu, Auteur ; Chariton Kalaitzidis, Auteur Année de publication : 2013 Article en page(s) : pp 114 - 129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification orientée objet [Termes IGN] classification Spectral angle mapper [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] image Quickbird [Termes IGN] littoral méditerranéen [Termes IGN] matrice d'erreur [Termes IGN] occupation du sol Résumé : (Auteur) In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. Numéro de notice : A2013-278 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.668950 Date de publication en ligne : 02/04/2012 En ligne : https://doi.org/10.1080/10106049.2012.668950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32416 [article]

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