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Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation / G. Blackburn in International Journal of Remote Sensing IJRS, vol 28 n°11-12 (June 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°11-12 (June 2007) . - pp 2831 - 2855 Titre : Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation Type de document : Article/Communication Auteurs : G. Blackburn, Auteur Année de publication : 2007 Article en page(s) : pp 2831 - 2855 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chlorophylle [Termes IGN] coefficient de rétrodiffusion [Termes IGN] image hyperspectrale [Termes IGN] modèle de simulation [Termes IGN] réflectance [Termes IGN] réflectance végétale [Termes IGN] transformation en ondelettes Résumé : (Auteur) This paper reports a new approach for quantifying vegetation pigment concentrations through wavelet decomposition of hyperspectral remotely sensed data. Wavelets are a group of functions that vary in complexity and mathematical properties, that are used to dissect data into different frequency components and then characterize each component with a resolution appropriate to its scale. Wavelet analysis of a reflectance spectrum is performed by scaling and shifting the wavelet function to produce wavelet coefficients that are assigned to different frequency components. By selecting appropriate wavelet coefficients, a spectral model can be established between the coefficients and biochemical concentrations. Hence, wavelet analysis has the potential to capture much more of the information contained within high-resolution spectra than previous approaches and offers the prospect of developing robust, generic methods for pigment determinations. The capabilities of the wavelet-based technique were examined using reflectance spectra and pigment data collected for a range of plant species at leaf and canopy scales. For the combined data set and all of the individual vegetation types, methods based on wavelet decomposition appreciably outperformed narrowband spectral indices and stepwise selection of narrowband reflectance. However, there was variation between vegetation types in the relative performance of the three different feature extraction techniques employed for selecting the wavelet coefficients for use in predictive models. There was also considerable variability in the performance of predictive models according to the wavelet function used for spectral decomposition and the optimum wavelet functions differed between vegetation types and between individual pigments within the same vegetation type. The research indicates that wavelet analysis holds promise for the accurate determination of chlorophyll a and b and the carotenoids, but further work is needed to refine the approach. Copyright Taylor & Francis Numéro de notice : A2007-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600928625 En ligne : https://doi.org/10.1080/01431160600928625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28674 [article]

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