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Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6431 - 6439 Titre : Robust minimum volume simplex analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Shaoquan Zhang, Auteur ; Alexander Agathos, Auteur ; Jun Li, Auteur Année de publication : 2017 Article en page(s) : pp 6431 - 6439 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] factorisation [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste Résumé : (Auteur) Most blind hyperspectral unmixing methods exploit convex geometry properties of hyperspectral data. The minimum volume simplex analysis (MVSA) is one of such methods, which, as many others, estimates the minimum volume (MV) simplex where the measured vectors live. MVSA was conceived to circumvent the matrix factorization step often implemented by MV-based algorithms and also to cope with outliers, which compromise the results produced by MV algorithms. Inspired by the recently proposed robust MV enclosing simplex (RMVES) algorithm, we herein introduce the robust MVSA (RMVSA), which is a version of MVSA robust to noise. As in RMVES, the robustness is achieved by employing chance constraints, which control the volume of the resulting simplex. RMVSA differs, however, substantially from RMVES in the way optimization is carried out. In this paper, we develop a linearization relaxation of the nonlinear chance constraints, which can greatly lighten the computational complex of chance constraint problems. The effectiveness of RMVSA is illustrated by comparing its performance with the state of the art. Numéro de notice : A2017-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2728104 En ligne : https://doi.org/10.1109/TGRS.2017.2728104 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88784 [article]

Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 5067 - 5082 Titre : Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Alexander Agathos, Auteur ; Daniela Zaharie, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5067 - 5082 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme du simplexe [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] décomposition du pixel [Termes IGN] image hyperspectrale [Termes IGN] implémentation (informatique) Résumé : (Auteur) Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers, their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i.e., complex scenes) and also for problems involving a high number of pixels (i.e., large scenes). Numéro de notice : A2015-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2417162 Date de publication en ligne : 21/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2417162 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77556 [article]

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