A genetic-programming-based method for hyperspectral data information extraction: agricultural applications

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 ultrogen 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. Index Terms--Compact Airborne Spectrographic Imager (CASI) sensor, crop nitrogen, feature selection, genetic programming (GP), hyperspectral remote sensing, precision farming, site-specific management, spectral vegetation indices (SVIs).