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== Reference ==
[[Brian J. Ross]], A.G. Gualtieri, F. Fueten, P. Budkewitsch: [[Hyperspectral Image Analysis Using Genetic Programming_2002 | Hyperspectral Image Analysis Using Genetic Programming]]. W.B.Langdon et al.: GECCO 2002, CA, Morgan Kaufmann. 2002. pp. 1196-1203.

== DOI ==

== Abstract ==
Genetic programming is used to evolve min-
eral identification functions for hyperspec-
tral images. The input image set comprises
168 images from different wavelengths rang-
ing from 428 nm (visible blue) to 2507 nm
(invisible shortwave in the infrared), taken
over Cuprite, Nevada, with the AVIRIS hy-
perspectral sensor. A composite mineral im-
age indicating the overall reflectance percent-
age of three minerals (alunite, kaolnite, bud-
dingtonite) is used as a reference or “solu-
tion” image. The training set is manually se-
lected 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 parameterized features such as thresh-
olded mineral reflectance intensity and target
GP language. The results are promising, es-
pecially for minerals with higher reflectance
thresholds (more intense concentrations).

== Extended Abstract ==

== Bibtex ==

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== Links ==
=== Full Text ===
http://www.cosc.brocku.ca/~bross/research/RWA008.pdf

[[intern file]]

=== Sonstige Links ===

Hyperspectral Image Analysis Using Genetic Programming 2002 - Versionsgeschichte

Gbachelier: Die Seite wurde neu angelegt: „== Reference == Brian J. Ross, A.G. Gualtieri, F. Fueten, P. Budkewitsch: Hyperspectral Image Analysis Using Genetic Programming_2002 | Hyperspectral Ima…“