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5th International Conference on Intelligent Systems Design and Applications (ISDA'05)
Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification
Wroclaw, Poland
September 08-September 10
ISBN: 0-7695-2286-6
ASCII Text
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Arnaud Quirin, Jerzy Korczak, Martin V. Butz, David E. Goldberg, "Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification," Intelligent Systems Design and Applications, International Conference on, pp. 280-285, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), 2005.
 
 
BibTex
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@article{ 10.1109/ISDA.2005.23,
author = {Arnaud Quirin and Jerzy Korczak and Martin V. Butz and David E. Goldberg},
title = {Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2286-6},
pages = {280-285},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2005.23},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification
SN - 0-7695-2286-6
SP280
EP285
A1 - Arnaud Quirin,
A1 - Jerzy Korczak,
A1 - Martin V. Butz,
A1 - David E. Goldberg,
PY - 2005
KW - null
VL - 0
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
Arnaud Quirin, Universite Louis Pasteur LSIIT, CNRS Strasbourg, France
Jerzy Korczak, Universite Louis Pasteur LSIIT, CNRS Strasbourg, France
Martin V. Butz, University of Illinois, US
David E. Goldberg, University of Illinois, US
In this article, two learning classifier systems based on evolutionary techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classifi- cation system XCSR. The two algorithms are detailed and validated on hyperspectral data.
Citation:
Arnaud Quirin, Jerzy Korczak, Martin V. Butz, David E. Goldberg, "Analysis and Evaluation of Learning Classifier Systems applied to Hyperspectral Image Classification," isda, pp.280-285, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), 2005
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