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Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > analyse des mélanges spectraux
analyse des mélanges spectrauxSynonyme(s)SMA démélange spectral |
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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)
[article]
Titre : Nonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization Type de document : Article/Communication Auteurs : Naoto Yokoya, Auteur ; Jocelyn Chanussot, Auteur ; Akira Iwasaki, Auteur Année de publication : 2014 Article en page(s) : pp 1430 - 1437 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] factorisation
[Termes IGN] image hyperspectrale
[Termes IGN] matrice
[Termes IGN] modèle non linéaireRésumé : (Auteur) Nonlinear spectral mixture models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel optimization method of nonlinear unmixing based on a generalized bilinear model (GBM), which considers the second-order scattering of photons in a spectral mixture model. Semi-nonnegative matrix factorization (semi-NMF) is used for the optimization to process a whole image in matrix form. When endmember spectra are given, the optimization of abundance and interaction abundance fractions converge to a local optimum by alternating update rules with simple implementation. The proposed method is evaluated using synthetic datasets considering its robustness for the accuracy of endmember extraction and spectral complexity, and shows smaller errors in abundance fractions rather than conventional methods. GBM-based unmixing using semi-NMF is applied to the analysis of an airborne hyperspectral image taken over an agricultural field with many endmembers, and it visualizes the impact of a nonlinear interaction on abundance maps at reasonable computational cost. Numéro de notice : A2014-076 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2245671 En ligne : https://doi.org/10.1109/TGRS.2013.2245671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32981
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1430 - 1437[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Structured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Structured sparse method for hyperspectral unmixing Type de document : Article/Communication Auteurs : Feiyun Zhu, Auteur ; Yin Wang, Auteur ; Shiming Xiang, Auteur ; Bin Fan, Auteur ; Chunhong Pan, Auteur Année de publication : 2014 Article en page(s) : pp 101 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] factorisation
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuse
[Termes IGN] programmation par contraintesRésumé : (Auteur) Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. Numéro de notice : A2014-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32992
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 101 - 118[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Collaborative sparse regression for hyperspectral unmixing / Marian-Daniel Iordache in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)
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Titre : Collaborative sparse regression for hyperspectral unmixing Type de document : Article/Communication Auteurs : Marian-Daniel Iordache, Auteur ; José Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2014 Article en page(s) : pp 341 - 354 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] régressionRésumé : (Auteur) Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called “multitask” or “simultaneous”) sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach. Numéro de notice : A2014-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2240001 En ligne : https://doi.org/10.1109/TGRS.2013.2240001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32943
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 341 - 354[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014011A RAB Revue Centre de documentation En réserve L003 Disponible Unmixing polarimetric radar images based on land cover type before target decomposition / Sébastien Giordano (2014)
Titre : Unmixing polarimetric radar images based on land cover type before target decomposition Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Grégoire Mercier, Auteur ; Jean-Paul Rudant , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : IGARSS 2014, International Geoscience And Remote Sensing Symposium 13/07/2014 18/07/2014 Québec Québec - Canada Proceedings IEEE Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image radar
[Termes IGN] matrice de covariance
[Termes IGN] polarimétrie radarRésumé : (auteur) A new method for unmixing radar polarimetric images with optical images is proposed. It was found that the polarimetric covariance matrix can be unmixed considering a linear model. As a result, this model is used to produce unmixed covariance matrices based on land cover types. We hope to prove that this unmixing of the polarimetric information produce greater information for land cover classification. Numéro de notice : C2014-043 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2014.6947055 Date de publication en ligne : 06/11/2014 En ligne : https://doi.org/10.1109/IGARSS.2014.6947055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99588 The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques / Chengbin Deng in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)
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Titre : The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques Type de document : Article/Communication Auteurs : Chengbin Deng, Auteur ; Changshan Wu, Auteur Année de publication : 2013 Article en page(s) : pp 100 - 110 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] image multibande
[Termes IGN] image Terra-MODIS
[Termes IGN] méthode des moindres carrés
[Termes IGN] surface imperméable
[Termes IGN] zone urbaineRésumé : (Auteur) Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available. Numéro de notice : A2013-705 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.09.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32841
in ISPRS Journal of photogrammetry and remote sensing > vol 86 (December 2013) . - pp 100 - 110[article]Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida / Fenqing Weng in Geocarto international, vol 28 n° 7-8 (November - December 2013)PermalinkDeblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkSpectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkManifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkPiecewise convex multiple-model endmember detection and spectral unmixing / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkSampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)PermalinkCrop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery / B. Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)PermalinkTotal variation spatial regularization for sparse hyperspectral unmixing / M. Iordache in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)PermalinkTriangular 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)PermalinkMultiple endmember unmixing of CHRIS/Proba imagery for mapping impervious surfaces in urban and suburban environments / Luca Demarchi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)Permalink