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A hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)  

Public [article]  inGeoinformatica > vol 15 n° 1 (January 2011) . - pp 29 - 47 Titre : A hybrid classification scheme for mining multisource geospatial data Type de document : Article/Communication Auteurs : R. Vatsavai, Auteur ; B. Bhaduri, Auteur Année de publication : 2011 Article en page(s) : pp 29 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification hybride [Termes IGN] classification par maximum de vraisemblance [Termes IGN] données auxiliaires [Termes IGN] exploration de données géographiques [Termes IGN] image Landsat [Termes IGN] image multibande [Termes IGN] précision de la classification Résumé : (Auteur) Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 * |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes. Numéro de notice : A2011-027 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-010-0113-4 Date de publication en ligne : 22/07/2010 En ligne : https://doi.org/10.1007/s10707-010-0113-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30808 [article]

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