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Machine Vision and Applications

, Volume 27, Issue 5, pp 625–635 | Cite as

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

  • Landry Benoit
  • Romain Benoit
  • Étienne Belin
  • Rodolphe Vadaine
  • Didier Demilly
  • François Chapeau-Blondeau
  • David RousseauEmail author
Special Issue Paper

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.

Keywords

Spectral imaging Information theory Plant imaging 

Notes

Acknowledgments

This work received support from the French Government supervised by the Agence Nationale de la Recherche in the framework of the program Investissements d’Avenir under reference ANR-11-BTBR-0007 (AKER program). Landry BENOIT gratefully acknowledges financial support from Angers Loire Metropole and GEVES-SNES for the preparation of his PhD.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Landry Benoit
    • 1
  • Romain Benoit
    • 1
  • Étienne Belin
    • 1
  • Rodolphe Vadaine
    • 2
  • Didier Demilly
    • 2
  • François Chapeau-Blondeau
    • 1
  • David Rousseau
    • 3
    Email author
  1. 1.Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS)Université d’AngersAngersFrance
  2. 2.GEVES, Station Nationale d’Essais de Semences (SNES)BeaucouzéFrance
  3. 3.CREATIS; CNRS UMR 5220; INSERM U1044; Université Lyon 1, INSA-LyonUniversité de LyonVilleurbanneFrance

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