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Satellite image classification using genetically guided fuzzy clustering with spatial information / S. Bandyopadhyay in International Journal of Remote Sensing IJRS, vol 26 n° 3 (February 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 3 (February 2005) . - pp 579 - 593 Titre : Satellite image classification using genetically guided fuzzy clustering with spatial information Type de document : Article/Communication Auteurs : S. Bandyopadhyay, Auteur Année de publication : 2005 Article en page(s) : pp 579 - 593 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse de groupement [Termes IGN] Bombay [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] image satellite [Termes IGN] pixel [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (Auteur) Land-cover classification of satellite images is an important task in analysis of remote sensing imagery. Segmentation is one of the widely used techniques in this regard. One of the important approaches for segmentation of an image is by clustering the pixels in the spectral domain, where pixels that share some common spectral property are put in the same group, or cluster. However, such spectral clustering completely ignores the spatial information contained in the pixels, which is often an important consideration for good segmentation of images. Moreover, the clustering algorithms often provide locally optimal solutions. In this paper, we propose to perform. image segmentation by a genetically guided unsupervised fuzzy clustering technique where some spatial information of the pixels is incorporated. Two ways of incorporating spatial information are suggested. The characteristic of this technique is that it is able to determine automatically the appropriate number of clusters without making any assumptions regarding the dataset. while attempting to provide globally near optimal solutions. In order to evolve the appropriate number of clusters, the chromosome encoding scheme is enhanced to incorporate the don't care symbol (#). Real-coded genetic algorithm with appropriatly defined operators is used. A cluster validity index is used as a measure of the value of the chromosomes. Results, both quantitative and qualitative are demonstrated for several images, including a satellite image of a part of the city of Mumbai. Numéro de notice : A2005-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331316432 En ligne : https://doi.org/10.1080/01431160512331316432 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27180 [article]

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