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image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
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Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
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Titre : Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery Type de document : Article/Communication Auteurs : L. Yan, Auteur ; X. Niu, Auteur Année de publication : 2014 Article en page(s) : pp 849 - 861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] angle d'incidence
[Termes IGN] classification Spectral angle mapper
[Termes IGN] détection de cible
[Termes IGN] distance euclidienne
[Termes IGN] image hyperspectrale
[Termes IGN] réduction
[Termes IGN] réflectance spectrale
[Termes IGN] végétationRésumé : In traditional manifold learning of hyperspectral imagery, distances among pixels are defined in terms of Euclidean distance, which is not necessarilly the best choice because of its sensitivity to variations in spectrum magnitudes. Selecting Laplacian Eignemaps (LE) as the test method, this paper studies the effects of distance metric selection in LE and proposes a spectral-angle-based LE method (LE-SA)to be compared against the traditional LE-based on Euclidean distance (LE-ED). Le-SA and LA-ED were applied to two airborne hyperspectral data sets and the dimensionlity-reduced data were quantitatively evalueted. Experimental results demonstrated that LE-SA is able to suppress the variations within the same type of features, such as variations in vegetation and those in illuminations due to shade orientations, and maintain a higher level of overall separability among different features than LE-ED. Further, the potential usage of a single LA-SA or LE-ED band for target detection is discussed. Numéro de notice : A2014-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.849 En ligne : https://doi.org/10.14358/PERS.80.9.849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74888
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 849 - 861[article]Télédétection multi-échelle des lacs depuis un aéronef ultraléger motorisé / Y. Akhtman in Géomatique suisse, vol 112 n° 9 (septembre 2014)
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Titre : Télédétection multi-échelle des lacs depuis un aéronef ultraléger motorisé Type de document : Article/Communication Auteurs : Y. Akhtman, Auteur ; D. Constantin, Auteur ; Martin Rehak, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 395 - 398 Note générale : Bibliographie Langues : Français (fre) Allemand (ger) Italien (ita) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] échelle géographique
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] Léman (Lac)
[Termes IGN] qualité des eaux
[Termes IGN] Sibérie
[Termes IGN] Suisse
[Termes IGN] Ultra Léger MotoriséRésumé : (Auteur) Le projet Léman-Baïkal, fruit d'une collaboration scientifique entre la Russie et la Suisse, a permis de développer une nouvelle plateforme de télédétection utilisant des techniques d'imagerie multispectrale et hyperspectrale. Cette plateforme a été testée avec succès sur un aéronef ultraléger motorisé aussi bien au-dessus des lacs que des terres. Cet article présente la méthodologie développée pour la plateforme de télédétection, ainsi que les premiers résultats. Numéro de notice : A2014-545 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : http://retro.seals.ch/digbib/view2?pid=geo-007:2014:112::528 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74161
in Géomatique suisse > vol 112 n° 9 (septembre 2014) . - pp 395 - 398[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 136-2014091 RAB Revue Centre de documentation En réserve L003 Disponible Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
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Titre : Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor Type de document : Article/Communication Auteurs : Helge Aasen, Auteur ; Martin Leon Gnyp, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 785 - 795 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] logiciel de corrélation
[Termes IGN] matriceRésumé : (Auteur) Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands.
We introduce the software HyperCor for automated pre-processing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix.
We apply this method to a large multi-temporal spectral li-brary to derive vegetation indices and related regression mod-els for rice biomass detection in the tillering, stem elongation, heading and across all growth stages. The models are cali¬brated with data from three consecutive years and validated with two other years. The results reveal that the multi-corre¬lation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage.Numéro de notice : A2014-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.745 En ligne : https://doi.org/10.14358/PERS.80.8.745 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73719
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 785 - 795[article]Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
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Titre : Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades Type de document : Article/Communication Auteurs : Caiyun Zhang, Auteur Année de publication : 2014 Article en page(s) : pp. 733 - 743 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie thématique
[Termes IGN] données lidar
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] image hyperspectrale
[Termes IGN] marais
[Termes IGN] semis de points
[Termes IGN] traitement d'image
[Termes IGN] végétationRésumé : (Auteur)This study explored a combination of hyperspectral and lidar systems for vegetation mapping in the Florida Everglades. A framework was designed to integrate two remotely sensed datasets and four data processing techniques. Lidar elevation and intensity features were extracted from the original point cloud data to avoid the errors and uncertainties in the raster-based lidar methods. Lidar significantly increased the classification accuracy compared with the application of hyperspectral data alone. Three lidar-derived features (elevation, intensity, and topography) had the same contributions in the classification. A synergy of hyperspectral imagery with all lidar-derived features achieved the best result with an overall accuracy of 86 percent and a Kappa value of 0.82 based on an ensemble analysis of three machine learning classifiers. Ensemble analysis did not significantly increase the classification accuracy, but it provided a complementary uncertainty map for the final classified map. The study shows the promise of the synergy of hyperspectral and lidar systems for mapping complex wetlands. Numéro de notice : A2014-344 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.733 En ligne : https://doi.org/10.14358/PERS.80.8.733 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73717
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 733 - 743[article]Deriving Predictive relationships of carotenoid content at the canopy level in a conifer forest using hyperspectral imagery and model simulation / Rocío Hernández-Clemente in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
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Titre : Deriving Predictive relationships of carotenoid content at the canopy level in a conifer forest using hyperspectral imagery and model simulation Type de document : Article/Communication Auteurs : Rocío Hernández-Clemente, Auteur ; R.M. Navarro Cerrillo, Auteur ; Pablo J. Zarco-Tejada, Auteur Année de publication : 2014 Article en page(s) : pp 5206 - 5217 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] canopée
[Termes IGN] chlorophylle
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] modélisation
[Termes IGN] photosynthèse
[Termes IGN] Pinophyta
[Termes IGN] réflectance végétale
[Termes IGN] surveillance forestièreRésumé : (Auteur) Recent studies have demonstrated that the R570/R515 index is highly sensitive to carotenoid (Cx + c) content in conifer forest canopies and is scarcely influenced by structural effects. However, validated methods for the prediction of leaf carotenoid content relationships in forest canopies are still needed to date. This paper focuses on the simultaneous retrieval of chlorophyll (Cα + b) and (Cx + c) pigments, which are critical bioindicators of plant physiological status. Radiative transfer theory and modeling assumptions were applied at both laboratory and field scales to develop methods for their concurrent estimation using high-resolution hyperspectral imagery. The proposed methodology was validated based on the biochemical pigment quantification. Canopy modeling methods based on infinite reflectance formulations and the discrete anisotropic radiative transfer (DART) model were evaluated in relation to the PROSPECT-5 leaf model for the scaling-up procedure. Simpler modeling methods yielded comparable results to more complex 3-D approximations due to the high spatial resolution images acquired, which enabled targeting pure crowns and reducing the effects of canopy architecture. The scaling-up methods based on the PROSPECT-5+DART model yielded a root-mean-square error (RMSE) and a relative RMSE of 1.48 μg/cm2 (17.45%) and 5.03 μg/cm2 (13.25%) for Cx + c and Cα + b, respectively, while the simpler approach based on the PROSPECT-5+Hapke infinite reflectance model yielded 1.37 μg/cm2 (17.46%) and 4.71 μg/cm2 (14.07%) for Cx + c and Cα+b, respectively. These predictive algorithms proved to be useful to estimate Cα + b and Cx + c from high-resolution hyperspectral imagery, providing a methodology for the monitoring of these photosynthetic pigments in conifer forest canopies. Numéro de notice : A2014-433 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287304 En ligne : https://doi.org/10.1109/TGRS.2013.2287304 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73970
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5206 - 5217[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species / Manjit Saini in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
PermalinkImproved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ / Mohamad Awad in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
PermalinkKernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
Permalinkvol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
PermalinkSpectral identification of materials by reflectance spectral library search / Rama Rao Nidamanuri in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
PermalinkNovel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing / Jaime Zabalza in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)
PermalinkDecision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkFeature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkSemisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkSubspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)
PermalinkDouble constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
PermalinkHyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
PermalinkSlow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
PermalinkWetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar / Lian P. Rampi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)
PermalinkBayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
PermalinkHyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
PermalinkImpact of signal contamination on the adaptive detection performance of local hyperspectral anomalies / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
PermalinkProgressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
PermalinkAbove ground biomass estimation in an African tropical forest with lidar and hyperspectral data / Gaia Vaglio Laurin in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
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