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Sampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 3 Tome 2 (March 2013) . - pp 1655 - 1665 Titre : Sampling piecewise convex unmixing and endmember extraction Type de document : Article/Communication Auteurs : Alina Zare, Auteur ; Paul Garder, Auteur ; George Casella, Auteur Année de publication : 2013 Article en page(s) : pp 1655 - 1665 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] échantillonnage d'image [Termes IGN] ensemble convexe [Termes IGN] image hyperspectrale [Termes IGN] méthode de Monte-Carlo [Termes IGN] signature spectrale Résumé : (Auteur) A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing methods, endmembers are generally represented as a single point in the high-dimensional space. However, the spectral signature for a material varies as a function of the inherent variability of the material and environmental conditions. Therefore, it is more appropriate to represent each endmember as a full distribution and use this information during spectral unmixing. The proposed method searches for several sets of endmember distributions. By using several sets of endmember distributions, a piecewise convex mixing model is applied, and given this model, the proposed method performs spectral unmixing and endmember estimation given this nonlinear representation of the data. Each set represents a random simplex. The vertices of the random simplex are modeled by the endmember distributions. The hyperspectral data are partitioned into sets associated with each of the extracted sets of endmember distributions using a Dirichlet process prior. The Dirichlet process prior also estimates the number of sets. Thus, the Metropolis-within-Gibbs sampler partitions the data into convex regions, estimates the required number of convex regions, and estimates endmember distributions and abundance values for all convex regions. Results are presented on real hyperspectral and simulated data that indicate the ability of the method to effectively estimate endmember distributions and the number of sets of endmember distributions. Numéro de notice : A2013-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2207905 En ligne : https://doi.org/10.1109/TGRS.2012.2207905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32272 [article]

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