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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]