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

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@Article{Chion:2008:ieeeTGRS,

• author = "Clement Chion and Jacques-Andre Landry and Luis {Da Costa}",
• title = "A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications",
• journal = "IEEE Transactions on Geoscience and Remote Sensing",
• year = "2008",
• month = aug,
• volume = "46",
• number = "8",
• pages = "2446--2457",
• keywords = "genetic algorithms, genetic programming, CASI sensor, agricultural application, band selection, canopy nitrogen content, crop biophysical variable, feature selection, genetic programming-spectral vegetation index, hyperspectral data information extraction, hyperspectral remote sensing, pixel reflectance, precision farming, crops, farming, feature extraction, geophysical signal processing, vegetation mapping",
• DOI = "doi:10.1109/TGRS.2008.922061",
• ISSN = "0196-2892",
• 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.",
• notes = "Also known as \cite{4559746}",

}

Genetic Programming entries for Clement Chion Jacques-Andre Landry Luis E Da Costa

Citations