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Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data / S. Jenicka in Survey review, vol 48 n° 351 (October 2016)  

Public [article]  inSurvey review > vol 48 n° 351 (October 2016) . - pp 430 - 437 Titre : Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data Type de document : Article/Communication Auteurs : S. Jenicka, Auteur ; A. Suruliandi, Auteur Année de publication : 2016 Article en page(s) : pp 430 - 437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image IRS-LISS [Termes IGN] modèle de Markov caché [Termes IGN] occupation du sol [Termes IGN] précision de la classification [Termes IGN] texture d'image Résumé : (Auteur) Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%. Numéro de notice : A2016-824 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1179/1752270615Y.0000000041 En ligne : https://doi.org/10.1179/1752270615Y.0000000041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82640 [article]