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Auteur L. Bruce |
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Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)
[article]
Titre : Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification Type de document : Article/Communication Auteurs : S. Prasad, Auteur ; J. Fowler, Auteur ; L. Bruce, Auteur ; W. Li, Auteur Année de publication : 2012 Article en page(s) : pp 3474 - 3486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] filtrage du bruit
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] partitionnement
[Termes IGN] redondance de données
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches. Numéro de notice : A2012-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185053 Date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31897
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3474 - 3486[article]Exemplaires(1)
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