Hyperspectral Image Analysis for Precision Viticulture

  • M. Ferreiro-Armán
  • J. -P. Da Costa
  • S. Homayouni
  • J. Martín-Herrero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


We analyze the capabilities of CASI data for the discrimination of vine varieties in hyperspectral images. To analyze the discrimination capabilities of the CASI data, principal components analysis and linear discriminant analysis methods are used. We assess the performance of various classification techniques: Multi-layer perceptrons, radial basis function neural networks, and support vector machines. We also discuss the trade-off between spatial and spectral resolutions in the framework of precision viticulture.


Support Vector Machine Linear Discriminant Analysis Radial Basis Function Neural Network Grape Variety Hyperspectral Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Ferreiro-Armán
    • 1
  • J. -P. Da Costa
    • 2
  • S. Homayouni
    • 2
  • J. Martín-Herrero
    • 1
  1. 1.Departamento de Teoría do Sinal e Comunicacións, ETSETUniversidade de VigoSpain
  2. 2.LAPS – UMR 5131 CNRSUniversité Bordeaux 1TalenceFrance

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