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Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3811 - 3821 Titre : Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach Type de document : Article/Communication Auteurs : Abderrahim Halimi, Auteur ; Paul Honeine, Auteur ; Malika Kharouf, Auteur ; Cédric Richard, Auteur ; Jean-Yves Tourneret, Auteur Année de publication : 2016 Article en page(s) : pp 3811 - 3821 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bruit blanc [Termes IGN] image hyperspectrale [Termes IGN] modèle de mélange multilinéaire [Termes IGN] valeur propre Résumé : (Auteur) Linear mixture models are commonly used to represent a hyperspectral data cube as linear combinations of endmember spectra. However, determining the number of endmembers for images embedded in noise is a crucial task. This paper proposes a fully automatic approach for estimating the number of endmembers in hyperspectral images. The estimation is based on recent results of random matrix theory related to the so-called spiked population model. More precisely, we study the gap between successive eigenvalues of the sample covariance matrix constructed from high-dimensional noisy samples. The resulting estimation strategy is fully automatic and robust to correlated noise owing to the consideration of a noise-whitening step. This strategy is validated on both synthetic and real images. The experimental results are very promising and show the accuracy of this algorithm with respect to state-of-the-art algorithms. Numéro de notice : A2016-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2528298 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2528298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83032 [article]