Abstract
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.
This work has been cofinanced with FEDER funds through the Interreg IIIb “Atlantic Area” program within the PIMHAI project.
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Ferreiro-Armán, M., Da Costa, J.P., Homayouni, S., Martín-Herrero, J. (2006). Hyperspectral Image Analysis for Precision Viticulture. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_66
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DOI: https://doi.org/10.1007/11867661_66
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