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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]Exemplaires(1)
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)
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Titre : Hyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species Type de document : Article/Communication Auteurs : Manjit Saini, Auteur ; Christian Binal, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 773 - 784 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] classification
[Termes IGN] forêt tropicale
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Inde
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] variation saisonnièreRésumé : (Auteur)Synchronizing hyperspectral data acquisition with phonological changes in a tropical forest can generate comprehensive information for their effective management. The present study was performed to identify a suitable dimensionality reduction method for better classification and to evaluate the impact of seasonally on classification accuracy of tropical forest cover. EO-1 Hyperion images were acquired for three different seasons (summer (April), monsoon (October), and winter (January)). Spectral signatures of pure patches of Teak, Bamboo, and mixed species covers are significantly different across the three seasons indicating distinctive phenology of each cover. Kernel Principal Component Analysis (k-PCA) is more suitable for dimensionality reduction for these covers. The three vegetation covers classified using images of three seasons achieved the best classification accuracies using k-PCA with maximum likeli-hood classifier for the monsoon season with overall accuracies of 83 to 100 percent for single species, 74 to 81 percent for two species, and 72 percent for three species respectively. Numéro de notice : A2014-345 Affiliation des auteurs : non IGN Thématique : FORET/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=73718
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp 773 - 784[article]Hyperspectral 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)
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Titre : Hyperspectral remote sensing image subpixel target detection based on supervised metric learning Type de document : Article/Communication Auteurs : Lefei Zhang, Auteur ; Liangpei Zhang, Auteur ; Dacheng Tao, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 4955 - 4965 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification dirigée
[Termes IGN] classification pixellaire
[Termes IGN] détection de cible
[Termes IGN] image hyperspectraleRésumé : (Auteur) The detection and identification of target pixels such as certain minerals and man-made objects from hyperspectral remote sensing images is of great interest for both civilian and military applications. However, due to the restriction in the spatial resolution of most airborne or satellite hyperspectral sensors, the targets often appear as subpixels in the hyperspectral image (HSI). The observed spectral feature of the desired target pixel (positive sample) is therefore a mixed signature of the reference target spectrum and the background pixels spectra (negative samples), which belong to various land cover classes. In this paper, we propose a novel supervised metric learning (SML) algorithm, which can effectively learn a distance metric for hyperspectral target detection, by which target pixels are easily detected in positive space while the background pixels are pushed into negative space as far as possible. The proposed SML algorithm first maximizes the distance between the positive and negative samples by an objective function of the supervised distance maximization. Then, by considering the variety of the background spectral features, we put a similarity propagation constraint into the SML to simultaneously link the target pixels with positive samples, as well as the background pixels with negative samples, which helps to reject false alarms in the target detection. Finally, a manifold smoothness regularization is imposed on the positive samples to preserve their local geometry in the obtained metric. Based on the public data sets of mineral detection in an Airborne Visible/Infrared Imaging Spectrometer image and fabric and vehicle detection in a Hyperspectral Mapper image, quantitative comparisons of several HSI target detection methods, as well as some state-of-the-art metric learning algorithms, were performed. All the experimental results demonstrate the effectiveness of the proposed SML algorithm for hyperspectral target detection. Numéro de notice : A2014-434 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286195 En ligne : https://doi.org/10.1109/TGRS.2013.2286195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73971
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 4955 - 4965[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Improved 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)
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Titre : Improved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ Type de document : Article/Communication Auteurs : Mohamad Awad, Auteur ; Ihab Jomaa, Auteur ; Fatima Arab, Auteur Année de publication : 2014 Article en page(s) : pp. 725 - 731 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] carte topographique
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-TM
[Termes IGN] image PROBA-CHRIS
[Termes IGN] Liban
[Termes IGN] Pinus (genre)
[Termes IGN] répartition géographique
[Termes IGN] traitement automatique d'imagesRésumé : (Auteur)The Stone Pine (pinus pinea) is native to the Mediterranean region and has been used for their edible pine nuts since prehistoric times. They are widespread in horticultural cultivation as ornamental trees and planted in gardens and parks around the world. Economically speaking, the Stone Pine is very important for the agriculture sector, for tourism, and for the health sector. In this research, a pilot area located in Mount Lebanon is compared for changes in the Stone Pine cover between the years of 1962 and 2012. The comparison is based on processing a hyperspectral image provided by the European Space Agency (ESA) and a Landsat ETM+ image as well as topographic maps. Several issues related to the use of CHRiS-Proba hyperspectral images have been investigated and analyzed. The results established that hyperspectral data: (a) is 30 percent or more accurate and efficient when compared with multispectral data, and (b) helps determine precise extent of the Stone Pine cover. Numéro de notice : A2014-343 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.725 En ligne : https://doi.org/10.14358/PERS.80.8.725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73713
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 725 - 731[article]Kernel 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)
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Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] décomposition en fonctions orthogonales empiriques
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] précision de la classification
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible vol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensingPermalinkSpectral 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)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)PermalinkSemi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)PermalinkUL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkUn vaste champ d'applications / Françoise de Blomac in DécryptaGéo le mag, n° 155 (01/03/2014)PermalinkEstimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China / Liguo Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkNonlinear 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)PermalinkStructured sparse method for hyperspectral unmixing / Feiyun Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)Permalink