Chion, Clement; Landry, J.A. and Da Costa, Luis E. 2008. A Genetic Programming Based Method for Hyperspectral Data Information Extraction: Agricultural Applications. IEEE Transactions on Geoscience and Remote Sensing. Volume 46, Issue 8, Aug. 2008 Page(s):2446 - 2457.
Abstract
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 bet- ter 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 hyperspec- tral data.
Index Terms—Compact Airborne Spectrographic Imager (CASI) sensor, crop nitrogen, feature selection, genetic program- ming (GP), hyperspectral remote sensing, precision farming, site-specific management, spectral vegetation indices (SVIs).