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A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications

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3 Author(s)
Chion, C. ; Ecole de Technol. Super., Quebec Univ., Montreal, QC ; Landry, J.-A. ; Da Costa, L.

A new method, called genetic programming-spectral vegetation index (GP-SVI), for the extraction of information from hyperspectral data is presented. This method is introduced in the context of precision farming. GP-SVI derives a regression model describing a specific crop biophysical variable from hyperspectral images (verified with in situ observations). GP-SVI performed better than other methods [multiple regression, tree-based modeling, and genetic algorithm-partial least squares (GA-PLS)] on the task of correlating canopy nitrogen content in a cornfield with pixel reflectance. It is also shown that the band selection performed by GP-SVI is comparable with the selection performed by GA-PLS, a method that is specifically designed to deal with hyperspectral data.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:46 ,  Issue: 8 )