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Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1213 - 1222 Titre : Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression Type de document : Article/Communication Auteurs : J. Walton, Auteur Année de publication : 2008 Article en page(s) : pp 1213 - 1222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] image Landsat-ETM+ [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] régression [Termes IGN] séparateur à vaste marge [Termes IGN] surface imperméable Résumé : (Auteur) Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (reflectance, tasseled cap, and both reflectance and tasseled cap plus thermal) were compared for their effectiveness with each of the methods. Thirty different training site number and size combinations were also tested. Support vector regression on the tasseled cap bands was found to be the best estimator for urban forest canopy cover, while Cubist performed best using the reflectance plus tasseled cap band combination when predicting impervious surface cover. More training data partitioned in many small training sites generally produces better estimation results. Copyright ASPRS Numéro de notice : A2008-374 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1213 En ligne : https://doi.org/10.14358/PERS.74.10.1213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29367 [article]