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Automatic fuzzy clustering using modified differential evolution for image classification / U. Maulik in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 9 (September 2010) . - pp 3503 - 3510 Titre : Automatic fuzzy clustering using modified differential evolution for image classification Type de document : Article/Communication Auteurs : U. Maulik, Auteur ; I. Saha, Auteur Année de publication : 2010 Article en page(s) : pp 3503 - 3510 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] identification automatique [Termes IGN] image satellite [Termes IGN] occupation du sol [Termes IGN] regroupement de données Résumé : (Auteur) The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types, some of which cover significantly large areas while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, a new real-coded modified differential evolution based automatic fuzzy clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Here, the assignment of points to different clusters is done based on a Xie-Beni index where the Euclidean distance is taken into consideration. The effectiveness of the proposed technique is first demonstrated for two numeric remote sensing data described in terms of feature vectors and then in identifying different landcover regions in remote sensing imagery. The superiority of the new method is demonstrated by comparing it with other existing techniques like automatic clustering using improved differential evolution, classical differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering, and well known fuzzy C-means algorithm both qualitatively and quantitatively. Numéro de notice : A2010-571 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2047020 En ligne : https://ieeexplore.ieee.org/document/5462924 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30762 [article]

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