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Auteur Helge Aasen |
Documents disponibles écrits par cet auteur (2)
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Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
[article]
Titre : Extracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography Type de document : Article/Communication Auteurs : Lukas Roth, Auteur ; Helge Aasen, Auteur ; Achim Walter, Auteur ; Frank Liebisch, Auteur Année de publication : 2018 Article en page(s) : pp 161 - 175 Note générale : Bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] cultures
[Termes IGN] drone
[Termes IGN] Glycine max
[Termes IGN] image aérienne
[Termes IGN] image RVB
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] modélisation géométrique de prise de vue
[Termes IGN] orthoimage géoréférencée
[Termes IGN] segmentation d'image
[Termes IGN] simulation 3D
[Termes IGN] SuisseRésumé : (Editeur) Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI prediction accuracy was comparable with the one of a gap fraction-based handheld device ( of , RMSE of m 2m−2) and correlated well with destructive LAI measurements ( of , RMSE of m2 m−2). These results indicate that, if respecting the range (LAI ) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science. Numéro de notice : A2018-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.04.012 Date de publication en ligne : 07/05/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.04.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90402
in ISPRS Journal of photogrammetry and remote sensing > vol 141 (July 2018) . - pp 161 - 175[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 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)
[article]
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]