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Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs / F. Giacco in IEEE Transactions on geoscience and remote sensing, vol 48 n° 10 (October 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 10 (October 2010) . - pp 3769 - 3779 Titre : Uncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs Type de document : Article/Communication Auteurs : F. Giacco, Auteur ; C. Thiel, Auteur ; L. Pugliese, Auteur ; S. Scarpetta, Auteur ; M. Marinaro, Auteur Année de publication : 2010 Article en page(s) : pp 3769 - 3779 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] carte de Kohonen [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image multibande [Termes IGN] incertitude des données Résumé : (Auteur) Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizing maps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results. Numéro de notice : A2010-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2047863 Date de publication en ligne : 27/05/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2047863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30668 [article]

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