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A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373 Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] reconnaissance automatique Résumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250 [article]

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065-06111B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Exploiting class hierarchies for knowledge transfer in hyperspectral data / S. Rajan in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3408 - 3417 Titre : Exploiting class hierarchies for knowledge transfer in hyperspectral data Type de document : Article/Communication Auteurs : S. Rajan, Auteur ; J. Ghosh, Auteur ; Melba M. Crawford, Auteur Année de publication : 2006 Article en page(s) : pp 3408 - 3417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification non dirigée [Termes IGN] données multitemporelles [Termes IGN] image hyperspectrale [Termes IGN] signature spectrale Résumé : (Auteur) Obtaining ground truth for classification of remotely sensed data is time consuming and expensive, resulting in poorly represented signatures over large areas. In addition, the spectral signatures of a given class vary with location and/or time. Therefore, successful adaptation of a classifier designed from the available labeled data to classify new hyperspectral images acquired over other geographic locations or subsequent times is difficult, if minimal additional labeled data are available. In this paper, the binary hierarchical classifier is used to propose a knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data. Experimental results show that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data. Moreover, when small amounts of the labeled data are available from the new area, the framework offers further improvements through semisupervised learning mechanisms and compares favorably with previously proposed methods. Copyright IEEE Numéro de notice : A2006-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.878442 En ligne : https://doi.org/10.1109/TGRS.2006.878442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28251 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-06111B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A novel method for mapping land cover changes: Incorporating time and space with geostatistics / A. Boucher in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3427 - 3435 Titre : A novel method for mapping land cover changes: Incorporating time and space with geostatistics Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; K.C. Seto, Auteur ; A.G. Journel, Auteur Année de publication : 2006 Article en page(s) : pp 3427 - 3435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification pixellaire [Termes IGN] détection de changement [Termes IGN] données de terrain [Termes IGN] filtre de déchatoiement [Termes IGN] géostatistique [Termes IGN] krigeage [Termes IGN] série temporelle [Termes IGN] utilisation du sol [Termes IGN] variogramme Résumé : (Auteur) Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps. Copyright IEEE Numéro de notice : A2006-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.879113 En ligne : https://doi.org/10.1109/TGRS.2006.879113 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28252 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-06111B	RAB	Revue	Centre de documentation	En réserve L003	Disponible