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Auteur S. Rajan |
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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)
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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 spectraleRésumé : (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
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3408 - 3417[article]Exemplaires(1)
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