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A Bayesian approach to classification of multiresolution remote sensing data / G. Storvik in IEEE Transactions on geoscience and remote sensing, vol 43 n° 3 (March 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 3 (March 2005) . - pp 539 - 547 Titre : A Bayesian approach to classification of multiresolution remote sensing data Type de document : Article/Communication Auteurs : G. Storvik, Auteur ; R. Fjortoft, Auteur ; A.H. Schistad, Auteur Année de publication : 2005 Article en page(s) : pp 539 - 547 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multiéchelle [Termes IGN] champ aléatoire de Markov [Termes IGN] classification [Termes IGN] estimation bayesienne [Termes IGN] image à basse résolution [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] limite de résolution géométrique [Termes IGN] modèle de Markov [Termes IGN] résolution multiple Résumé : (Auteur) Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This paper presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Result obtained on simulated and real satellite images are presented. Numéro de notice : A2005-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.841395 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1396326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27305 [article]

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