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A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Aug2008, Vol. 46 Issue 8, p2446-2457, 12p
- Publication Year :
- 2008
-
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 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 46
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 34045896
- Full Text :
- https://doi.org/10.1109/TGRS.2008.922061