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analyse des mélanges spectrauxSynonyme(s)SMA démélange spectral |
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Multi-objective based spectral unmixing for hyperspectral images / Xia Xu in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)
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Titre : Multi-objective based spectral unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xia Xu, Auteur ; Zhenwei Shi, Auteur Année de publication : 2017 Article en page(s) : pp 54 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)Résumé : (Auteur) Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method. Numéro de notice : A2017-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.12.010 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.12.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84306
in ISPRS Journal of photogrammetry and remote sensing > vol 124 (February 2017) . - pp 54 - 69[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017023 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017022 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Modeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)
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Titre : Modeling spatial and temporal variabilities in hyperspectral image unmixing Type de document : Thèse/HDR Auteurs : Pierre-Antoine Thouvenin, Auteur ; Nicolas Dobigeon, Directeur de thèse ; Jean-Yves Tourneret, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2017 Importance : 191 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] amplitude
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données multitemporelles
[Termes IGN] image hyperspectrale
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processus stochastique
[Termes IGN] séparation aveugle de source
[Termes IGN] signature spectrale
[Termes IGN] variabilitéIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data – referred to as endmembers – and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape
and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.Note de contenu : Introduction
1- Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
2- A Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images
3- Online unmixing of multitemporal hyperspectral images
4- A partially asynchronous distributed unmixing algorithm
Conclusions et perspectivesNuméro de notice : 25812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2017 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017INPT0068 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95075 Multiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
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Titre : Multiband image fusion based on spectral unmixing Type de document : Article/Communication Auteurs : Qi Wei, Auteur ; José Bioucas-Dias, Auteur ; Nicolas Dobigeon, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7236 - 7249 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] fusion d'images
[Termes IGN] image à basse résolution
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] matrice de covarianceRésumé : (Auteur) This paper presents a multiband image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial-low-spectral-resolution image and a low-spatial-high-spectral-resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The nonnegativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is then formulated as maximizing the joint posterior distribution with respect to the endmember signatures and abundance maps. This optimization problem is attacked with an alternating optimization strategy. The two resulting subproblems are convex and are solved efficiently using the alternating direction method of multipliers. Experiments are conducted for both synthetic and semi-real data. Simulation results show that the proposed unmixing-based fusion scheme improves both the abundance and endmember estimation compared with the state-of-the-art joint fusion and unmixing algorithms. Numéro de notice : A2016-930 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598784 En ligne : https://doi.org/10.1109/TGRS.2016.2598784 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83344
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7236 - 7249[article]Blind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Blind hyperspectral unmixing using total variation and ℓq sparse regularization Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6371 - 6384 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] régularisation d'image
[Termes IGN] simulation d'image
[Termes IGN] variableRésumé : (Auteur) Blind hyperspectral unmixing involves jointly estimating endmembers and fractional abundances in hyperspectral images. An endmember is the spectral signature of a specific material in an image, while an abundance map specifies the amount of a material seen in each pixel in an image. In this paper, a new cyclic descent algorithm for blind hyperspectral unmixing using total variation (TV) and ℓq sparse regularization is proposed. Abundance maps are both spatially smooth and sparse. Their sparsity derives from the fact that each material in the image is not represented in all pixels. The abundance maps are assumed to be piecewise smooth since adjacent pixels in natural images tend to be composed of similar material. The TV regularizer is used to encourage piecewise smooth images, and the ℓq regularizer promotes sparsity. The dyadic expansion decouples the problem, making a cyclic descent procedure possible, where one abundance map is estimated, followed by the estimation of one endmember. A novel debiasing technique is also employed to reduce the bias of the algorithm. The algorithm is evaluated using both simulated and real hyperspectral images. Numéro de notice : A2016-914 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2582824 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2582824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83136
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6371 - 6384[article]Influence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)
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Titre : Influence of tree species complexity on discrimination performance of vegetation indices Type de document : Article/Communication Auteurs : Azadeh Ghiyamat, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Abdul Rashid Mohamed Shariff, Auteur Année de publication : 2016 Article en page(s) : pp 15 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse discriminante
[Termes IGN] espèce végétale
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] information complexe
[Termes IGN] Pinus nigra corsicana
[Termes IGN] Pinus sylvestris
[Termes IGN] test de performanceRésumé : (auteur) Performance of different vegetation indices (VIs) in combination with single- and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%. Numéro de notice : A2016-834 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164902 En ligne : http://dx.doi.org/10.5721/EuJRS20164902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82723
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 15 - 37[article]Robust collaborative nonnegative matrix factorization for hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkEstimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkSemiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkSimultaneously 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)
PermalinkA superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
PermalinkUnsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)
PermalinkComparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
PermalinkThin cloud removal based on signal transmission principles and spectral mixture analysis / Meng Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. Saranathan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
PermalinkApport de la prise en compte de la variabilité intra-classe dans les méthodes de démélange hyperspectral pour l'imagerie urbaine / Charlotte Revel (2016)
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