%0 Report %1 ross2:2002:geccoTR %A Ross, Brian J. %A Gualtieri, Anthony G. %A Fueten, Frank %A Budkewitsch, Paul %D 2002 %K algorithms, genetic programming %N CS-02-12 %T Hyperspectral Image Analysis Using Genetic Programming %U http://citeseer.ist.psu.edu/523309.html %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 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.

@techreport{ross2:2002:geccoTR, 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.}, 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/27d084a27c1844726799d333f68d5b0f6/brazovayeye}, institution = {Department of Computer Science, Brock University}, interhash = {a2d9c57bf6670355422dbdf3f5e50126}, intrahash = {7d084a27c1844726799d333f68d5b0f6}, keywords = {algorithms, genetic programming}, month = May, number = {CS-02-12}, size = {9 pages. See also \cite{ross2:2002:gecco}}, timestamp = {2008-06-19T17:50:41.000+0200}, title = {Hyperspectral Image Analysis Using Genetic Programming}, type = {Technical Report}, url = {http://citeseer.ist.psu.edu/523309.html}, year = 2002 }