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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]

Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 178 - 188 Titre : Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration Type de document : Article/Communication Auteurs : Wei He, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur ; Huanfeng Shen, Auteur Année de publication : 2016 Article en page(s) : pp 178 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] factorisation [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] restauration d'image Résumé : (Auteur) In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L1-norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is detected by the L1-norm regularization. To tradeoff the nuclear norm and TV regularization and to further remove the Gaussian noise of the HSI, we also restrict the rank of the clean image to be no larger than the number of endmembers. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed LRTV method for HSI restoration. Numéro de notice : A2016-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2452812 En ligne : https://doi.org/10.1109/TGRS.2015.2452812 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79834 [article]

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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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Double constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2746 - 2758 Titre : Double constrained NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Hao Wu, Auteur ; Yuan Yuan, Auteur Année de publication : 2014 Article en page(s) : pp 2746 - 2758 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] contrainte spectrale [Termes IGN] factorisation [Termes IGN] factorisation de matrice non-négative [Termes IGN] image hyperspectrale Résumé : (Auteur) Given only the collected hyperspectral data, unmixing aims at obtaining the latent constituent materials and their corresponding fractional abundances. Recently, many nonnegative matrix factorization (NMF)-based algorithms have been developed to deal with this issue. Considering that the abundances of most materials may be sparse, the sparseness constraint is intuitively introduced into NMF. Although sparse NMF algorithms have achieved advanced performance in unmixing, the result is still susceptible to unstable decomposition and noise corruption. To reduce the aforementioned drawbacks, the structural information of the data is exploited to guide the unmixing. Since similar pixel spectra often imply similar substance constructions, clustering can explicitly characterize this similarity. Through maintaining the structural information during the unmixing, the resulting fractional abundances by the proposed algorithm can well coincide with the real distributions of constituent materials. Moreover, the additional clustering-based regularization term also lessens the interference of noise to some extent. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art algorithms. Numéro de notice : A2014-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2265322 En ligne : https://doi.org/10.1109/TGRS.2013.2265322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33166 [article]

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Spatial and spectral image fusion using sparse matrix factorization / Bo Huang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 3 (March 2014) . - pp 1693 - 1704 Titre : Spatial and spectral image fusion using sparse matrix factorization Type de document : Article/Communication Auteurs : Bo Huang, Auteur ; Huihui Song, Auteur ; Hengbin Cui, Auteur ; Jigen Peng, Auteur ; Zongben Xu, Auteur Année de publication : 2014 Article en page(s) : pp 1693 - 1704 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse combinatoire (maths) [Termes IGN] apprentissage automatique [Termes IGN] factorisation [Termes IGN] fusion d'images [Termes IGN] image Landsat-ETM+ [Termes IGN] image Terra-MODIS [Termes IGN] matrice creuse [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale Résumé : (Auteur) In this paper, we present a novel spatial and spectral fusion model (SASFM) that uses sparse matrix factorization to fuse remote sensing imagery with different spatial and spectral properties. By combining the spectral information from sensors with low spatial resolution (LSaR) but high spectral resolution (HSeR) (hereafter called HSeR sensors), with the spatial information from sensors with high spatial resolution (HSaR) but low spectral resolution (LSeR) (hereafter called HSaR sensors), the SASFM can generate synthetic remote sensing data with both HSaR and HSeR. Given two reasonable assumptions, the proposed model can integrate the LSaR and HSaR data via two stages. In the first stage, the model learns from the LSaR data a spectral dictionary containing pure signatures, and in the second stage, the desired HSaR and HSeR data are predicted using the learned spectral dictionary and the known HSaR data. The SASFM is tested with both simulated data and actual Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and it is also compared to other representative algorithms. The experimental results demonstrate that the SASFM outperforms other algorithms in generating fused imagery with both the well-preserved spectral properties of MODIS and the spatial properties of ETM+. Generated imagery with simultaneous HSaR and HSeR opens new avenues for applications of MODIS and ETM+. Numéro de notice : A2014-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2253612 En ligne : https://doi.org/10.1109/TGRS.2013.2253612 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33020 [article]

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Nonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization / Naoto Yokoya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

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Structured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

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Manifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

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Triangular factorization-based simplex algorithms for hyperspectral unmixing / W. Xia in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)  

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Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data / A. Huck in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)  

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Apports d'une conception orientée-objet à la résolution numérique des équations de Maxwell dans le cadre d'une méthodologie de factorisation / D. Caron (2000)

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