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The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)  

Public [article]  inGeocarto international > vol 32 n° 1 (January 2017) . - pp 71 - 86 Titre : The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery Type de document : Article/Communication Auteurs : Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 71 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] arbre de décision [Termes IGN] classification orientée objet [Termes IGN] classification par arbre de décision [Termes IGN] classification pixellaire [Termes IGN] image Worldview [Termes IGN] régression logistique Résumé : (auteur) Logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel- and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel- and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon’s Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel- and object-based image classifications. Numéro de notice : A2017-085 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1128486 Date de publication en ligne : 12/01/2016 En ligne : http://dx.doi.org/10.1080/10106049.2015.1128486 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84410 [article]

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