%0 Conference Paper %1 ross2:2002:gecco %A Ross, Brian J. %A Gualtieri, Anthony G. %A Fueten, Frank %A Budkewitsch, Paul %B GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference %C New York %D 2002 %E Langdon, W. B. %E Cantú-Paz, E. %E Mathias, K. %E Roy, R. %E Davis, D. %E Poli, R. %E Balakrishnan, K. %E Honavar, V. %E Rudolph, G. %E Wegener, J. %E Bull, L. %E Potter, M. A. %E Schultz, A. C. %E Miller, J. F. %E Burke, E. %E Jonoska, N. %I Morgan Kaufmann Publishers %K algorithms, applications, classification genetic hyperspectral imaging, mineral programming, real world %P 1196--1203 %T Hyperspectral Image Analysis Using Genetic Programming %U http://citeseer.ist.psu.edu/503556.html %X Genetic programming is used to evolve mineral identification functions for hyperspectral images. The input image set comprises 168 images from di#erent 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 di#erent GP experiments were undertaken, which parameterized features such as thresholded mineral reflectance intensity and target GP language. The results are promising, especially for minerals with higher reflectance thresholds (more intense concentrations). %@ 1-55860-878-8

@inproceedings{ross2:2002:gecco, abstract = {Genetic programming is used to evolve mineral identification functions for hyperspectral images. The input image set comprises 168 images from di#erent 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 di#erent GP experiments were undertaken, which parameterized features such as thresholded mineral reflectance intensity and target GP language. The results are promising, especially for minerals with higher reflectance thresholds (more intense concentrations).}, added-at = {2008-06-19T17:35:00.000+0200}, address = {New York}, author = {Ross, Brian J. and Gualtieri, Anthony G. and Fueten, Frank and Budkewitsch, Paul}, biburl = {https://www.bibsonomy.org/bibtex/260cb6b574d02444f6488090e730d4c1f/brazovayeye}, booktitle = {GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference}, editor = {Langdon, W. B. and Cant{\'u}-Paz, E. and Mathias, K. and Roy, R. and Davis, D. and Poli, R. and Balakrishnan, K. and Honavar, V. and Rudolph, G. and Wegener, J. and Bull, L. and Potter, M. A. and Schultz, A. C. and Miller, J. F. and Burke, E. and Jonoska, N.}, interhash = {a2d9c57bf6670355422dbdf3f5e50126}, intrahash = {60cb6b574d02444f6488090e730d4c1f}, isbn = {1-55860-878-8}, keywords = {algorithms, applications, classification genetic hyperspectral imaging, mineral programming, real world}, month = {9-13 July}, notes = {GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002) See also \cite{ross2:2002:geccoTR}}, pages = {1196--1203}, publisher = {Morgan Kaufmann Publishers}, publisher_address = {San Francisco, CA 94104, USA}, timestamp = {2008-06-19T17:50:41.000+0200}, title = {Hyperspectral Image Analysis Using Genetic Programming}, url = {http://citeseer.ist.psu.edu/503556.html}, year = 2002 }