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Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery / D.A. Clausi in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 215 - 228 Titre : Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery Type de document : Article/Communication Auteurs : D.A. Clausi, Auteur ; B. Yue, Auteur Année de publication : 2004 Article en page(s) : pp 215 - 228 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] champ aléatoire de Markov [Termes IGN] glace [Termes IGN] image Radarsat [Termes IGN] niveau de gris (image) [Termes IGN] segmentation d'image [Termes IGN] télédétection spatiale [Termes IGN] texture d'image Résumé : (Auteur) This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive in texture boundary confusion than MRFs given their respective segmentation procedures. Numéro de notice : A2004-045 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817218 En ligne : https://doi.org/10.1109/TGRS.2003.817218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26573 [article]

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