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.
%0 Journal Article
%1 ross:2005:ASC
%A Ross, Brian J.
%A Gualtieri, Anthony G.
%A Fueten, Frank
%A Budkewitsch, Paul
%D 2005
%J Applied Soft Computing
%K Hyperspectral Mineral algorithms, analysis, genetic identification image programming,
%N 2
%P 147--156
%R doi:10.1016/j.asoc.2004.06.003
%T Hyperspectral image analysis using genetic
programming
%U http://www.sciencedirect.com/science/article/B6W86-4D2FFK0-1/2/90bc9108d9351bf3a5bc1011f3d43493
%V 5
%X 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.
@article{ross:2005:ASC,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Ross, Brian J. and Gualtieri, Anthony G. and Fueten, Frank and Budkewitsch, Paul},
biburl = {https://www.bibsonomy.org/bibtex/21af15f66f7e98d86518023fa6ac3bb56/brazovayeye},
doi = {doi:10.1016/j.asoc.2004.06.003},
interhash = {d98a924c7432a617c707d20905f49048},
intrahash = {1af15f66f7e98d86518023fa6ac3bb56},
journal = {Applied Soft Computing},
keywords = {Hyperspectral Mineral algorithms, analysis, genetic identification image programming,},
month = {January},
number = 2,
pages = {147--156},
timestamp = {2008-06-19T17:50:42.000+0200},
title = {Hyperspectral image analysis using genetic
programming},
url = {http://www.sciencedirect.com/science/article/B6W86-4D2FFK0-1/2/90bc9108d9351bf3a5bc1011f3d43493},
volume = 5,
year = 2005
}