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Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4597 - 4608 Titre : Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation Type de document : Article/Communication Auteurs : A. Agrawal, Auteur ; N. Kumar, Auteur ; M. Radhakrishna, Auteur Année de publication : 2007 Article en page(s) : pp 4597 - 4608 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] ERDAS Imagine [Termes IGN] image IRS-LISS [Termes IGN] image multibande [Termes IGN] incertitude des données [Termes IGN] Inde [Termes IGN] Kappa de Cohen [Termes IGN] Perceptron multicouche [Termes IGN] sous ensemble flou Résumé : (Auteur) A supervised neural network classification model based on rough-fuzzy membership function, weak fuzzy similarity relation, multilayer perceptron, and back-propagation algorithm is proposed. The described model is capable of dealing with rough uncertainty as well as fuzzy uncertainty associated with the classification of multispectral images. The concept of weak fuzzy similarity relation is used for generation of fuzzy equivalence classes during the calculation of rough-fuzzy membership function. The model allows efficient modelling of indiscernibility and fuzziness between patterns by appropriate weights being assigned using the back-propagated errors depending upon the rough-fuzzy membership values at the corresponding outputs. The effectiveness of the proposed model is demonstrated on classification problem of IRS-P6 LISS IV image of Allahabad area. The results are compared with statistical (minimum distance to means), conventional Multi-Layer Perceptron (MLP) and Fuzzy Multi-Layer Perceptron (FMLP) models. The better overall accuracy, user's and producer's accuracies and kappa coefficient of the proposed classifier in comparison to other considered models demonstrate the effectiveness of this model in multispectral image classification. Copyright Taylor & Francis Numéro de notice : A2007-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244898 En ligne : https://doi.org/10.1080/01431160701244898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28812 [article]

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