Centre de documentation
scientifique

Dépouillements

Attraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2799 - 2814 Titre : Attraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery Type de document : Article/Communication Auteurs : Xiaohua Tong, Auteur ; Xue Zhang, Auteur ; Jie Shan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 2799 - 2814 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] affinage d'image [Termes IGN] analyse infrapixellaire [Termes IGN] décomposition du pixel [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] programmation linéaire [Termes IGN] reconstruction d'image Résumé : (Auteur) This paper presents a new subpixel mapping method based on subpixel attraction-repulsion. The proposed method is formulated as an optimization problem with respect to attraction-repulsion among subpixels and is used to reconstruct a finer spatial resolution image from a lower resolution one. A comprehensive experiment is conducted to demonstrate the performance of the proposed method, by comparing it with the other three existing subpixel mapping methods, i.e., linear optimization, pixel swapping and spatial attraction model methods. In the experiment, both a synthetic image with known fractional abundances and an EO-1 Hyperion hyperspectral image of Shanghai were used to evaluate performances of the subpixel mapping methods. The experimental result shows that by using spatial dependence with attraction between the same types of ground objects and repulsion between different types of these objects, the proposed subpixel mapping method achieves a better performance on subpixel mapping than the other three methods Numéro de notice : A2013-257 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2218612 En ligne : https://doi.org/10.1109/TGRS.2012.2218612 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32395 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2815 - 2826 Titre : Manifold regularized sparse NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaqiang Lu, Auteur ; Hao Wu, Auteur ; Pingkun Yan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 2815 - 2826 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] factorisation de matrice non-négative [Termes IGN] image hyperspectrale [Termes IGN] matrice creuse Résumé : (Auteur) Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. Recently, many sparse nonnegative matrix factorization (NMF) algorithms have achieved advanced performance for hyperspectral unmixing because they overcome the difficulty of absence of pure pixels and sufficiently utilize the sparse characteristic of the data. However, most existing sparse NMF algorithms for hyperspectral unmixing only consider the Euclidean structure of the hyperspectral data space. In fact, hyperspectral data are more likely to lie on a low-dimensional submanifold embedded in the high-dimensional ambient space. Thus, it is necessary to consider the intrinsic manifold structure for hyperspectral unmixing. In order to exploit the latent manifold structure of the data during the decomposition, manifold regularization is incorporated into sparsity-constrained NMF for unmixing in this paper. Since the additional manifold regularization term can keep the close link between the original image and the material abundance maps, the proposed approach leads to a more desired unmixing performance. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art approaches. Numéro de notice : A2013-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2213825 En ligne : https://doi.org/10.1109/TGRS.2012.2213825 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32396 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2827 - 2836 Titre : A sparse image fusion algorithm with application to pan-sharpening Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Richard Bamler, Auteur Année de publication : 2013 Article en page(s) : pp 2827 - 2836 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] compression d'image [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image Geoeye [Termes IGN] image Ikonos [Termes IGN] image panchromatique [Termes IGN] image Quickbird [Termes IGN] méthode robuste [Termes IGN] pansharpening (fusion d'images) Résumé : (Auteur) Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse Fusion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capability and robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods. Numéro de notice : A2013-259 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2213604 En ligne : https://doi.org/10.1109/TGRS.2012.2213604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32397 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Models and methods for automated background density estimation in hyperspectral anomaly detection / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2837 - 2852 Titre : Models and methods for automated background density estimation in hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Stefania Matteoli, Auteur ; Tiziana Veracini, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 2837 - 2852 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] automatisation [Termes IGN] détection d'anomalie [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] prise en compte du contexte Résumé : (Auteur) Anomaly detection (AD) in remotely sensed hyperspectral images has been proven to be valuable in many applications. In this paper, we propose a scheme for detecting global anomalies in which a likelihood ratio test-based decision rule is applied in conjunction with automated data-driven estimation of the background probability density function (PDF). Specifically, the use of both semiparametric (finite mixtures) and nonparametric (Parzen windows) models is investigated for background PDF estimation. Although such approaches are well known in multivariate data analysis, they have been very seldom applied to estimate the hyperspectral image background PDF, mostly due to the difficulty of reliably learning the model parameters without operator intervention. In this paper, semi and nonparametric estimators have been successfully employed to estimate the image background PDF with the aim of detecting global anomalies in a scene benefiting from the application of ad hoc Bayesian learning strategies. Two real hyperspectral images have been used to experimentally evaluate the ability of the proposed AD scheme resulting from the application of different global background PDF models and learning methods. Numéro de notice : A2013-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2214392 En ligne : https://doi.org/10.1109/TGRS.2012.2214392 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32398 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Piecewise 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2853 - 2862 Titre : Piecewise convex multiple-model endmember detection and spectral unmixing Type de document : Article/Communication Auteurs : Alina Zare, Auteur Année de publication : 2013 Article en page(s) : pp 2853 - 2862 Note générale : Bibliographie 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] image hyperspectrale Résumé : (Auteur) A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The results indicate that the piecewise convex representation estimates endmembers that better represent hyperspectral imagery composed of multiple regions where each region is represented with a distinct set of endmembers Numéro de notice : A2013-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2219058 En ligne : https://doi.org/10.1109/TGRS.2012.2219058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32399 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A classification algorithm for hyperspectral images based on synergetics theory / Daniele Cerra in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2887 - 2898 Titre : A classification algorithm for hyperspectral images based on synergetics theory Type de document : Article/Communication Auteurs : Daniele Cerra, Auteur ; Rupert Müller, Auteur ; Peter Reinartz, Auteur Année de publication : 2013 Article en page(s) : pp 2887 - 2898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] classification [Termes IGN] image hyperspectrale Résumé : (Auteur) This paper presents a classification methodology for hyperspectral data based on synergetics theory. Pattern recognition algorithms based on synergetics have been applied to images in the spatial domain with limited success in the past, given their dependence on the rotation, shifting, and scaling of the images. These drawbacks can be discarded if such methods are applied to data acquired by a hyperspectral sensor in the spectral domain, as each single spectrum, related to an image element in the hyperspectral scene, can be analyzed independently. The spectrum is first projected in a space spanned by a set of user-defined prototype vectors, which belong to some classes of interest, and then attracted by a final state associated to a prototype. The spectrum can thus be classified, establishing a first attempt at performing a pixel-wise image classification using notions derived from synergetics. As typical synergetics-based systems have the drawback of a rigid training step, we introduce a new procedure which allows the selection of a training area for each class of interest, used to weight the prototype vectors through attention parameters and to produce a more accurate classification map through plurality vote of independent classifications. As each classification is in principle obtained on the basis of a single training sample per class, the proposed technique could be particularly effective in tasks where only a small training data set is available. The results presented are promising and often outperform state-of-the-art classification methodologies, both general and specific to hyperspectral data. Numéro de notice : A2013-269 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2219059 En ligne : https://doi.org/10.1109/TGRS.2012.2219059 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32407 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013051A	RAB	Revue	Centre de documentation	En réserve L003	Disponible