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Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process / A. Rizvi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 12 Tome 1 (December 2011) . - pp 4815 - 4820 Titre : Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process Type de document : Article/Communication Auteurs : A. Rizvi, Auteur ; B. Mohan, Auteur Année de publication : 2011 Article en page(s) : pp 4815 - 4820 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] estimation de précision [Termes IGN] fonction de base radiale [Termes IGN] image à haute résolution [Termes IGN] processus stochastique [Termes IGN] segmentation sémantique Résumé : (Auteur) Object-based image analysis is quickly gaining acceptance among remote sensing community, and object-based image classification methods are increasingly being used for classification of land use/cover units from high-resolution satellite images with results closer to human interpretation compared to per-pixel classifiers. The problem of nonlinear separability of classes in a feature space consisting of spectral/spatial/textural features is addressed by kernel-based nonlinear mapping of the feature vectors. This facilitates use of linear discriminant functions for classification as used in artificial neural networks (ANNs). In this paper, performance of a recently introduced kernel called cloud basis function (CBF) is investigated with some modification for classification. The CBF has demonstrated superior performance to the tune of about 4% higher classification accuracy compared to conventional radial basis function used in ANN. The results are further improved by using probabilistic relaxation labeling as a postprocessing step. This paper has potential applications in urban planning and urban studies. Numéro de notice : A2011-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171695 Date de publication en ligne : 22/12/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171695 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31373 [article]

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