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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]

Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing / Paris V. Giampouras in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4775 - 4789 Titre : Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing Type de document : Article/Communication Auteurs : Paris V. Giampouras, Auteur ; Konstantinos E. Themelis, Auteur ; Athanasios A. Rontogiannis, Auteur ; Konstantinos D. Koutroumbas, Auteur Année de publication : 2016 Article en page(s) : pp 4775 - 4789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] corrélation automatique de points homologues [Termes IGN] données clairsemées [Termes IGN] image hyperspectrale [Termes IGN] matrice creuse Résumé : (Auteur) In a plethora of applications dealing with inverse problems, e.g., image processing, social networks, compressive sensing, and biological data processing, the signal of interest is known to be structured in several ways at the same time. This premise has recently guided research into the innovative and meaningful idea of imposing multiple constraints on the unknown parameters involved in the problem under study. For instance, when dealing with problems whose unknown parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low rankness is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced in an attempt to exploit both spatial correlation and sparse representation of pixels lying in the homogeneous regions of hyperspectral images. To this end, a novel mixed penalty term is first defined consisting of the sum of the weighted ℓ1 and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an incremental proximal sparse and low-rank unmixing algorithm; and 2) an algorithm based on the alternating direction method of multipliers. The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data. Numéro de notice : A2016-891 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2551327 En ligne : https://doi.org/10.1109/TGRS.2016.2551327 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83071 [article]

On estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)    

Public [article]  inElectronic Journal of Statistics > vol 10 n° 1 (January 2016) . - pp 1551 - 1579 Titre : On estimation of the diagonal elements of a sparse precision matrix Type de document : Article/Communication Auteurs : Samuel Balmand , Auteur ; Arnak Dalalyan, Auteur Année de publication : 2016 Article en page(s) : pp 1551 - 1579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] calcul matriciel [Termes IGN] estimateur [Termes IGN] matrice creuse [Termes IGN] matrice de covariance [Termes IGN] matrice diagonale [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] régression linéaire [Termes IGN] résidu Résumé : (Auteur) In this paper, we present several estimators of the diagonal elements of the inverse of the covariance matrix, called precision matrix, of a sample of independent and identically distributed random vectors. The main focus is on the case of high dimensional vectors having a sparse precision matrix. It is now well understood that when the underlying distribution is Gaussian, the columns of the precision matrix can be estimated independently form one another by solving linear regression problems under sparsity constraints. This approach leads to a computationally efficient strategy for estimating the precision matrix that starts by estimating the regression vectors, then estimates the diagonal entries of the precision matrix and, in a final step, combines these estimators for getting estimators of the off-diagonal entries. While the step of estimating the regression vector has been intensively studied over the past decade, the problem of deriving statistically accurate estimators of the diagonal entries has received much less attention. The goal of the present paper is to fill this gap by presenting four estimators —that seem the most natural ones— of the diagonal entries of the precision matrix and then performing a comprehensive empirical evaluation of these estimators. The estimators under consideration are the residual variance, the relaxed maximum likelihood, the symmetry-enforced maximum likelihood and the penalized maximum likelihood. We show, both theoretically and empirically, that when the aforementioned regression vectors are estimated without error, the symmetry-enforced maximum likelihood estimator has the smallest estimation error. However, in a more realistic setting when the regression vector is estimated by a sparsity-favoring computationally efficient method, the qualities of the estimators become relatively comparable with a slight advantage for the residual variance estimator. Numéro de notice : A2016--107 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1214/16-EJS1148 Date de publication en ligne : 31/05/2016 En ligne : http://dx.doi.org/10.1214/16-EJS1148 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84707 [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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015061	SL	Revue	Centre de documentation	Revues en salle	Disponible

Interferometric phase image estimation via sparse coding in the complex domain / Hao Hongxing in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2587 - 2602 Titre : Interferometric phase image estimation via sparse coding in the complex domain Type de document : Article/Communication Auteurs : Hao Hongxing, Auteur ; José M. Bioucas-Dias, Auteur ; Vladimir Katkovnik, Auteur Année de publication : 2015 Article en page(s) : pp 2587 - 2602 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] état de l'art [Termes IGN] filtrage du bruit [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] matrice creuse [Termes IGN] phase [Termes IGN] programmation par contraintes [Termes IGN] régression [Termes IGN] restauration d'image Résumé : (auteur) This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches, and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed the atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data that it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the ℓ1 norm. The effectiveness of the new sparse-coding-based approach to interferometric phase estimation, termed the SpInPHASE, is illustrated in a series of experiments with simulated and real data where it outperforms the state-of-the-art. Numéro de notice : A2015-630 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361919 En ligne : https://doi.org/10.1109/TGRS.2014.2361919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78118 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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)  

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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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Modélisation d'un réseau de mares et évaluation de la connectivité selon la théorie des graphes / A. Thulie in Géomatique expert, n° 41- 42 (01/03/2005)

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A processing strategy for the application of the GPS in networks / P.J. de Jonge (1998)  

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Iteractive methods for sparse linear systems / Y. Saad (1996)

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Untersuchungen zur Lösung und Inversion schwach besetzter großer geodätischer Normalgleichungen / W. Stark (1984)

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Computer solution of large sparse positive definite systems / A. George (1981)

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Darstellung und Vergleiche von Lösungsstrategien für große schwach besetzte Normalgleichungssysteme in der Geodäsie und in der Photogrammetrie / F. Steidler (1980)

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Ausgleichung großer geodätischer Netze mit Verfahren für schwach besetzte Matrizen / Hans-Jörg Schek (1977)

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