Hyperspectral image analysis using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{ross:2005:ASC,
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author = "Brian J. Ross and Anthony G. Gualtieri and
Frank Fueten and Paul Budkewitsch",
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title = "Hyperspectral image analysis using genetic
programming",
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journal = "Applied Soft Computing",
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year = "2005",
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volume = "5",
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number = "2",
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pages = "147--156",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Hyperspectral
image analysis, Mineral identification",
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URL = "http://www.cosc.brocku.ca/~bross/research/gp_hyper.pdf",
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URL = "http://www.sciencedirect.com/science/article/B6W86-4D2FFK0-1/2/90bc9108d9351bf3a5bc1011f3d43493",
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DOI = "doi:10.1016/j.asoc.2004.06.003",
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abstract = "Genetic programming is used to evolve mineral
identification functions for hyperspectral images. The
input image set comprises 168 images from different
wavelengths ranging from 428nm (visible blue) to 2507nm
(invisible shortwave in the infrared), taken over
Cuprite, Nevada, with the AVIRIS hyper spectral sensor.
A composite mineral image indicating the overall
reflectance percentage of three minerals (alunite,
kaolnite, buddingtonite) is used as a reference or
{"}solution{"} image. The training set is manually
selected from this composite image, and results are
cross-validated with the remaining image data not used
for training. The task of the GP system is to evolve
mineral identifiers, where each identifier is trained
to identify one of the three mineral specimens. A
number of different GP experiments were undertaken,
which parameterised features such as thresholded
mineral reflectance intensity and target GP language.
The results are promising, especially for minerals with
higher reflectance thresholds, which indicate more
intense concentrations.",
- }
Genetic Programming entries for
Brian J Ross
Anthony G Gualtieri
Frank Fueten
Paul Budkewitsch
Citations