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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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 725 - 731 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'images Résumé : (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 [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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 733 - 743 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] végétation Résumé : (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 [article]

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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp 773 - 784 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 saisonnière Résumé : (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 [article]

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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 785 - 795 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] matrice Résumé : (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 [article]