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Substance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 2975 - 2986 Titre : Substance dependence constrained sparse NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Min Fu, Auteur ; Xiaoqiang Lu, Auteur Année de publication : 2015 Article en page(s) : pp 2975 - 2986 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification barycentrique [Termes IGN] état de l'art [Termes IGN] factorisation [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] matrice creuse Résumé : (Auteur) Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances. Recently, various methods have been proposed to incorporate sparse constraints into hyperspectral unmixing and achieve advanced performance. However, most of them ignore the complex distribution of substances in hyperspectral data so that they are only effective in limited cases. In this paper, the concept of substance dependence is introduced to help hyperspectral unmixing. Generally, substance dependence can be considered in a local region by K-nearest neighbors method. However, since substances of hyperspectral images are complicatedly distributed, number K of the most similar substances to each substance is difficult to decide. In this case, substance dependence should be considered in the whole data space, and the number of the K most similar substances to each substance can be adaptively determined by searching from the whole space. Through maintaining the substance dependence during unmixing, the abundances resulted from the proposed method are closer to the real fractions, which lead to better unmixing performance. The following contributions can be summarized. 1) The concept of substance dependence is proposed to describe the complicated relationship between substances in the hyperspectral image. 2) We propose substance dependence constrained sparse nonnegative matrix factorization (SDSNMF) for hyperspectral unmixing. Using SDSNMF, we meet or exceed state-of-the-art unmixing performance. 3) Adequate experiments on both synthetic and real hyperspectral data have been tested. Compared with the state-of-the-art methods, the experimental results prove the superiority of the proposed method. Numéro de notice : A2015-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365953 Date de publication en ligne : 13/01/2015 En ligne : https://doi.org/10.1109/TGRS.2014.2365953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76391 [article]

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