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Robust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1227 - 1239 Titre : Robust sparse hyperspectral unmixing with ℓ2,1 norm Type de document : Article/Communication Auteurs : Yong Ma, Auteur ; Chang Li, Auteur ; Xiaoguang Mei, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1227 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] matrice creuse [Termes IGN] méthode robuste [Termes IGN] pondération Résumé : (Auteur) Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the ℓ2 norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with ℓ2,1 norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms. Numéro de notice : A2017-150 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2616161 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84681 [article]