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Training set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)  

Public [article]  inRemote sensing of environment > vol 104 n° 1 (15/09/2006) . - pp 1 - 14 Titre : Training set size requirements for the classification of a specific class Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; A. Mathur, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 1 - 14 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Gossypium (genre) [Termes IGN] Inde [Termes IGN] intelligence artificielle [Termes IGN] réduction géométrique Résumé : (Auteur) The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of not, vert, similar 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at not, vert, similar 95% and not, vert, similar 97% from the user's and producer's perspectives respectively. Copyright Elsevier Numéro de notice : A2006-392 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.004 En ligne : https://doi.org/10.1016/j.rse.2006.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28116 [article]