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Spectral 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)
[article]
Titre : Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Shizhe Wang, Auteur ; Xiuping Jia, Auteur Année de publication : 2013 Article en page(s) : pp 3968 - 3981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espace de Hilbert
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. Numéro de notice : A2013-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227757 En ligne : https://doi.org/10.1109/TGRS.2012.2227757 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32509
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3968 - 3981[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Utility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)
[article]
Titre : Utility of the wavelet transform for LAI estimation using hyperspectral data Type de document : Article/Communication Auteurs : Asim Banskota, Auteur ; Randolph H. Wynne, Auteur ; Shawn P. Serbin, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : 662 p. ; pp 653 - 662 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme génétique
[Termes IGN] forêt tempérée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] transformation en ondelettes
[Termes IGN] Wisconsin (Etats-Unis)Résumé : (Auteur) We employed the discrete wavelet transform to reflectance spectra obtained from hyperspectral data to improve estimation of LAi in temperate forests. We estimated LAl for 32 plots across a range afforest types in Wisconsin using hemispherical photography. Plot spectra were extracted from AVIRIS data and transformed into wavelet features using the Haar wavelet. Separately, subsets of spectral bands and the Haar features selected by a genetic algorithm were used as independent variables in linear regressions. Models using wavelet coefficients explained the most variance for both broadleaf plots (R2 = 0.90 for wavelet features versus R2 = 0.80 for spectral bands) and all plots independent afforest type (R2 = 0.79 for wavelet features vs. R2 = 0.58 for spectral bands). The forest-type specific models were better than the models using all plots combined. Overall, wavelet features appear superior to band reflectances alone for estimating temperate forest LAI using hyperspectral data. Numéro de notice : A2013-394 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.7.653 En ligne : https://doi.org/10.14358/PERS.79.7.653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32532
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 7 (July 2013) . - 662 p. ; pp 653 - 662[article]View generation for multiview maximum disagreement based active learning for hyperspectral image classification / W. Di in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)
[article]
Titre : View generation for multiview maximum disagreement based active learning for hyperspectral image classification Type de document : Article/Communication Auteurs : W. Di, Auteur ; Melba M. Crawford, Auteur Année de publication : 2012 Article en page(s) : pp 1942 - 1954 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de pointsRésumé : (Auteur) Active learning (AL) seeks to interactively construct a smaller training data set that is the most informative and useful for the supervised classification task. Based on the multiview Adaptive Maximum Disagreement AL method, this study investigates the principles and capability of several approaches for the view generation for hyperspectral data classification, including clustering, random selection, and uniform subset slicing methods, which are then incorporated with dynamic view updating and feature space bagging strategies. Tests on Airborne Visible/Infrared Imaging Spectrometer and Hyperion hyperspectral data sets show excellent performance as compared with random sampling and the simple version support vector machine margin sampling, a state-of-the-art AL method. Numéro de notice : A2012-189 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2168566 En ligne : https://doi.org/10.1109/TGRS.2011.2168566 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31636
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 5 Tome 2 (May 2012) . - pp 1942 - 1954[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2012051B RAB Revue Centre de documentation En réserve L003 Disponible Pixel unmixing in hyperspectral data by means of neural networks / Giorgio Licciardi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)
[article]
Titre : Pixel unmixing in hyperspectral data by means of neural networks Type de document : Article/Communication Auteurs : Giorgio Licciardi, Auteur ; F. Del Frate, Auteur Année de publication : 2011 Article en page(s) : pp 4163 - 4172 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes principales
[Termes IGN] classification par réseau neuronal
[Termes IGN] image AHS
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image PROBA-CHRIS
[Termes IGN] réduction géométrique
[Termes IGN] test de performanceRésumé : (Auteur) Neural networks (NNs) are recognized as very effective techniques when facing complex retrieval tasks in remote sensing. In this paper, the potential of NNs has been applied in solving the unmixing problem in hyperspectral data. In its complete form, the processing scheme uses an NN architecture consisting of two stages: the first stage reduces the dimension of the input vector, while the second stage performs the mapping from the reduced input vector to the abundance percentages. The dimensionality reduction is performed by the so-called autoassociative NNs, which yield a nonlinear principal component analysis of the data. The evaluation of the whole performance is carried out for different sets of experimental data. The first one is provided by the Airborne Hyperspectral Scanner. The second set consists of images from the Compact High-Resolution Imaging Spectrometer on board the Project for On-Board Autonomy satellite, and it includes multiangle and multitemporal acquisitions. The third set is represented by Airborne Visible/InfraRed Imaging Spectrometer measurements. A quantitative performance analysis has been carried out in terms of effectiveness in the dimensionality reduction phase and in terms of the accuracy in the final estimation. The results obtained, when compared with those produced by appropriate benchmark techniques, show the advantages of this approach. Numéro de notice : A2011-445 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2160950 Date de publication en ligne : 01/08/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2160950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31223
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 11 Tome 1 (November 2011) . - pp 4163 - 4172[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2011111A RAB Revue Centre de documentation En réserve L003 Disponible Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)PermalinkFeature extraction of hyperspectral images using wavelet and matching pursuit / Pai-Hui Hsu in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)PermalinkMapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS / D.A. Fuentes in Remote sensing of environment, vol 103 n° 3 (15 August 2006)PermalinkNoise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage / H. Othman in IEEE Transactions on geoscience and remote sensing, vol 44 n° 2 (February 2006)PermalinkApplication of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA / L. Li in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)PermalinkQuality criteria benchmark for hyperspectral imagery / E. Christophe in IEEE Transactions on geoscience and remote sensing, vol 43 n° 9 (September 2005)PermalinkAn automatic nonlinear correlation approach for processing of hyperspectral images / R.N. Ingram in International Journal of Remote Sensing IJRS, vol 25 n° 22 (November 2004)PermalinkClassification of hyperspectral remote sensing images with support vector machines / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 42 n° 8 (August 2004)PermalinkLinear mixture analysis-based compression for hyperspectral image analysis / Q. Du in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)PermalinkEffect of grain size on remotely sensed spectral reflectance of sandy desert surfaces / G.S. Okin in Remote sensing of environment, vol 89 n° 3 (15/02/2004)Permalink