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A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 9 (September 2002) . - pp 1984 - 1996 Titre : A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; R. Cossu, Auteur Année de publication : 2002 Article en page(s) : pp 1984 - 1996 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] classification non dirigée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] image multitemporelle [Termes IGN] mise à jour cartographique Résumé : (Auteur) A system for a regular updating of landcover maps is proposed that is based on the use of multitemporal remote sensing images. Such a system is able to address the updating problem under the realistic but critical constraint that, for the image to be classified (i.e., the most recent of the considered multitemporal dataset no ground truth information is available. The system is composed of an ensemble of partially unsupervised classifiers integrated in a multipleclassifier architecture. Each classifier of the ensemble exhibits the following novel characteristics: 1) it is developed in the framework of the cascade-classification approach to exploit the temporal correlation existing between images acquired at different times in the considered area; and 2) it is based on a partially unsupervised methodology capable of accomplishing the classification process under the aforementioned critical constraint. Both a parametric maximumlikelihood (ML) classification approach and a nonparametric radial basis function (RBF) neuralnetwork classification approach are used as basic methods for the development of partially unsupervised cascade classifiers. In addition, in order to generate an effective ensemble of classification algorithms, hybrid ML and RBF neuralnetwork cascade classifiers are defined by exploiting the characteristics of the cascadeclassification methodology. The results yielded by the different classifiers are combined by using standard unsupervised combination strategies. This allows the definition of a robust and accurate partially unsupervised classification system capable of analyzing a wide typology of remote sensing data (e.g., images acquired by passive sensors, synthetic aperture radar images, and multisensor and multisource data). Experimental results obtained on a real multitemporal and multisource dataset confirm the effectiveness of the proposed system. Numéro de notice : A2002-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.803794 En ligne : https://doi.org/10.1109/TGRS.2002.803794 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22198 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Impact of contextual information integration on pixel fusion / Sophie Fabre in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 9 (September 2002) . - pp 1997 - 2010 Titre : Impact of contextual information integration on pixel fusion Type de document : Article/Communication Auteurs : Sophie Fabre, Auteur ; Xavier Briottet , Auteur ; A. Appriou, Auteur Année de publication : 2002 Article en page(s) : pp 1997 - 2010 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur non paramétrique [Termes IGN] classification de Dempster-Shafer [Termes IGN] fusion d'images [Termes IGN] méthode [Termes IGN] pixel [Termes IGN] prise en compte du contexte [Termes IGN] théorie de Dempster-Shafer [Termes IGN] vapeur d'eau Résumé : (Auteur) Pixel fusion is used to elaborate a classification method at pixel level. It needs to take into account the more accurate as possible information and take advantage of the statistical learning of the previous measurements acquired by sensors. The classical probabilistic fusion methods lack performance when the previous learning is not representative of the real measurements provided by sensors. The DempsterShafer theory is then introduced to face this disadvantage by integrating a further information which is the context of the sensor acquisitions. In this paper, we propose a formalism of modeling of the sensor reliability to the context that leads to two methods of integration: the first one amounts to integrate this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as mass function. These two methods are compared in the case where the sensor reliability depends on an atmospheric disturbance : the water vapor. Numéro de notice : A2002-288 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.805143 En ligne : https://doi.org/10.1109/TGRS.2002.805143 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22199 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02081	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Vector-lifting schemes for lossless coding and progressive archival of multispectral images / A. Benazza-Benyahia in IEEE Transactions on geoscience and remote sensing, vol 40 n° 9 (September 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 9 (September 2002) . - pp 2011 - 2024 Titre : Vector-lifting schemes for lossless coding and progressive archival of multispectral images Type de document : Article/Communication Auteurs : A. Benazza-Benyahia, Auteur ; J.C. Pesquet, Auteur ; Mohamed-Ali Hamdi, Auteur Année de publication : 2002 Article en page(s) : pp 2011 - 2024 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] codage [Termes IGN] espace vectoriel [Termes IGN] image multibande Résumé : (Auteur) In this paper, a nonlinear subband decomposition scheme with perfect reconstruction is proposed for lossless and progressive coding of multispectral images. The merit of this new scheme is to exploit efficiently the spatial and the spectral redundancies contained in the multispectral images related to a scene of interest. Besides, the proposed method is suitable for telebrowsing applications. Experiments carried out on real scenes allow to assess its performances. The simulation results demonstrate that our approach leads to improved compression performances compared with currently used lossless coders. Numéro de notice : A2002-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2002.803845 En ligne : https://doi.org/10.1109/TGRS.2002.803845 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22200 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02081	RAB	Revue	Centre de documentation	En réserve L003	Disponible