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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 5/2016

01.07.2016 | Special Issue Paper

On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

verfasst von: Landry Benoit, Romain Benoit, Étienne Belin, Rodolphe Vadaine, Didier Demilly, François Chapeau-Blondeau, David Rousseau

Erschienen in: Machine Vision and Applications | Ausgabe 5/2016

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Abstract

The practical value of a criterion based on statistical information theory is demonstrated for the selection of optimal wavelength and bandwidth of low-cost lighting systems in plant imaging applications. Kullback–Leibler divergence is applied to the problem of spectral band reduction from hyperspectral imaging. The results are illustrated on various plant imaging problems and show similar results to the one obtained with state-of-the-art criteria. A specific interest of the proposed approach is to offer the possibility to integrate technological constraints in the optimization of the spectral bands selected.
Vorheriger Artikel Generation and application of hyperspectral 3D plant models: methods and challenges
Nächster Artikel Automated estimation of tiller number in wheat by ribbon detection

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Metadaten
Titel
On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands
verfasst von
Landry Benoit
Romain Benoit
Étienne Belin
Rodolphe Vadaine
Didier Demilly
François Chapeau-Blondeau
David Rousseau
Publikationsdatum
01.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 5/2016
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-015-0717-7

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