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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]

Geometric unmixing of large hyperspectral images: A barycentric coordinate approach / Paul Honeine in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2185 - 2195 Titre : Geometric unmixing of large hyperspectral images: A barycentric coordinate approach Type de document : Article/Communication Auteurs : Paul Honeine, Auteur ; C. Richard, Auteur Année de publication : 2012 Article en page(s) : pp 2185 - 2195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du simplexe [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification barycentrique [Termes IGN] image hyperspectrale Résumé : (Auteur) In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure spectra, called endmembers, and their contributions, called abundances. Many endmember extraction techniques have been proposed in literature, based on either a statistical or a geometrical formulation. However, most, if not all, of these techniques for estimating abundances use a least-squares solution. In this paper, we show that abundances can be estimated using a geometric formulation. To this end, we express abundances with the barycentric coordinates in the simplex defined by endmembers. We propose to write them in terms of a ratio of volumes or a ratio of distances, which are quantities that are often computed to identify endmembers. This property allows us to easily incorporate abundance estimation within conventional endmember extraction techniques, without incurring additional computational complexity. We use this key property with various endmember extraction techniques, such as N-Findr, vertex component analysis, simplex growing algorithm, and iterated constrained endmembers. The relevance of the method is illustrated with experimental results on real hyperspectral images. Numéro de notice : A2012-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2188408 Date de publication en ligne : 14/11/2011 En ligne : https://doi.org/10.1109/TGRS.2012.2188408 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31709 [article]

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