%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 }