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Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields / R. Fjortoft in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 3 (March 2003) . - pp 675 - 686 Titre : Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields Type de document : Article/Communication Auteurs : R. Fjortoft, Auteur ; Y. Delignon, Auteur ; W. Pieczynski, Auteur ; M. Sigelle, Auteur ; Florence Tupin, Auteur Année de publication : 2003 Article en page(s) : pp 675 - 686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] chaîne de Markov [Termes IGN] champ aléatoire de Markov [Termes IGN] classification non dirigée [Termes IGN] estimation statistique [Termes IGN] image radar Résumé : (Auteur) Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can he determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented. Numéro de notice : A2003-119 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.809940 En ligne : https://doi.org/10.1109/TGRS.2003.809940 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22415 [article]

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