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A stochastic framework for the identification of building rooftops using a single remote sensing image / A. Katartzis in IEEE Transactions on geoscience and remote sensing, vol 46 n° 1 (January 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 1 (January 2008) . - pp 259 - 271 Titre : A stochastic framework for the identification of building rooftops using a single remote sensing image Type de document : Article/Communication Auteurs : A. Katartzis, Auteur ; H. Sahli, Auteur Année de publication : 2008 Article en page(s) : pp 259 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] champ aléatoire de Markov [Termes IGN] détection de contours [Termes IGN] géométrie projective [Termes IGN] image aérienne [Termes IGN] image isolée [Termes IGN] modèle stochastique [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) The identification of building rooftops from a single image, without the use of auxiliary 3-D information like stereo pairs or digital elevation models, is a very challenging and difficult task in the area of remote sensing. The existing methodologies rarely tackle the problem of 3-D object identification, like buildings, from a purely stochastic viewpoint. Our approach is based on a stochastic image interpretation model, which combines both 2-D and 3-D contextual information of the imaged scene. Building rooftop hypotheses are extracted using a contour-based grouping hierarchy that emanates from the principles of perceptual organization. We use a Markov random field model to describe the dependencies between all available hypotheses with regard to a globally consistent interpretation. The hypothesis verification step is treated as a stochastic optimization process that operates on the whole grouping hierarchy to find the globally optimal configuration for the locally interacting grouping hypotheses, providing also an estimate of the height of each extracted rooftop. This paper describes the main principles of our method and presents building detection results on a set of synthetic and airborne images. Copyright IEEE Numéro de notice : A2008-045 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.904953 En ligne : https://doi.org/10.1109/TGRS.2007.904953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29040 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-08011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Geostatistical solutions for super-resolution land cover mapping / A. Boucher in IEEE Transactions on geoscience and remote sensing, vol 46 n° 1 (January 2008)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 46 n° 1 (January 2008) . - pp 272 - 283 Titre : Geostatistical solutions for super-resolution land cover mapping Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; P. Kyriakidis, Auteur ; C. Cronkite-Ratcliff, Auteur Année de publication : 2008 Article en page(s) : pp 272 - 283 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] géostatistique [Termes IGN] spatiocarte [Termes IGN] variogramme Résumé : (Auteur) Super-resolution land cover mapping aims at producing fine spatial resolution maps of land cover classes from a set of coarse-resolution class fractions derived from satellite information via, for example, spectral unmixing procedures. Based on a prior model of spatial structure or texture that encodes the expected patterns of classes at the fine (target) resolution, this paper presents a sequential simulation framework for generating alternative super-resolution maps of class labels that are consistent with the coarse class fractions. Two modes of encapsulating the prior structural information are investigated-one uses a set of indicator variogram models, and the other uses training images. A case study illustrates that both approaches lead to super-resolution class maps that exhibit a variety of spatial patterns ranging from simple to complex. Using four different examples, it is demonstrated that the structural model controls the patterns seen on the super-resolution maps, even for cases where the coarse fraction data are highly constraining. Copyright IEEE Numéro de notice : A2008-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.907102 En ligne : https://doi.org/10.1109/TGRS.2007.907102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29041 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-08011	RAB	Revue	Centre de documentation	En réserve L003	Disponible