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Partially supervised classification of remote sensing images through SVM-based probability density estimation / P. Mantero in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 559 - 570 Titre : Partially supervised classification of remote sensing images through SVM-based probability density estimation Type de document : Article/Communication Auteurs : P. Mantero, Auteur ; G. Moser, Auteur ; S.B. Serpico, Auteur Année de publication : 2005 Article en page(s) : pp 559 - 570 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] estimation statistique [Termes IGN] probabilités [Termes IGN] réalité de terrain Résumé : (Auteur) A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used. Numéro de notice : A2005-168 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.842022 En ligne : https://doi.org/10.1109/TGRS.2004.842022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27306 [article]

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