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Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection / P.C. Smits in IEEE Transactions on geoscience and remote sensing, vol 40 n° 4 (April 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 4 (April 2002) . - pp 801 - 813 Titre : Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection Type de document : Article/Communication Auteurs : P.C. Smits, Auteur Année de publication : 2002 Article en page(s) : pp 801 - 813 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] manipulation dynamique de données Résumé : (Auteur) In the literature, multiple classifier systems (MCSs) have proved to be a valuable approach to combining classifiers, and under some conditions MCSs are able to mimic ideal Bayesian labeling. This paper focuses on the family of MCSs based on dynamic classifier selection (DCS) and proposes a modification to dynamic classifier selection by local accuracy (DCS-LA). Experiments show that the proposed method outperform MCS strategies based on belief functions and the DCS-LA in terms of minimum and maximum class accuracies and kappa coefficient of agreement and is a valid alternative to majority voting. Moreover, the experiments show that MCSs based on the classification results of classifiers characterized by a low design complexity like maximum likelihood and nearest mean classifiers can yield accuracies that are quite comparable to those of highly optimized classifiers. Numéro de notice : A2002-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.1006354 En ligne : https://doi.org/10.1109/TGRS.2002.1006354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22092 [article]

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