Hyperspectral Image Analysis Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @TechReport{ross2:2002:geccoTR,
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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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institution = "Department of Computer Science, Brock University",
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year = "2002",
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type = "Technical Report",
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number = "CS-02-12",
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month = may,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cosc.brocku.ca/Department/Research/TR/cs0212.pdf",
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URL = "http://citeseer.ist.psu.edu/523309.html",
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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 428 nm (visible blue) to 2507
nm (invisible shortwave in the infrared), taken over
Cuprite, Nevada, with the AVIRIS hyperspectral 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. 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 re ectance thresholds (more
intense concentrations). One complication in using this
technology is the time and expertise required to
interpret the data. Hyperspectral imaging systems such
as the NASA/JPL AVIRIS 1 sensor can capture over 200
bandwidths for a single geographic location (Green et
al. 1998). This is denoted by a hyperspectral cube,
which takes the form of many hundreds of mega-bytes of
information. Interpreting this massive amount of data
is difficult, especially considering that the spectra
obtained represent mixed spectral signatures of a
variety of materials. Moreover, noise and other
unwanted effects must be considered. Deciphering this
enormous volume of cryptic data is therefore next to
impossible for humans to do manually.",
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size = "9 pages. See also \cite{ross2:2002:gecco}",
- }
Genetic Programming entries for
Brian J Ross
Anthony G Gualtieri
Frank Fueten
Paul Budkewitsch
Citations