%0 Journal Article %1 oai:CiteSeerPSU:561309 %A Harvey, Neal R. %A Theiler, James %A Brumby, Steven P. %A Perkins, Simon %A Szymanski, John J. %A Bloch, Jeffrey J. %A Porter, Reid B. %A Galassi, Mark %A Young, A. Cody %D 2002 %J IEEE Transactions on Geoscience and Remote Sensing %K Algorithms, Classification, Evolutionary Exploitation, GENIE, GENetic IR, Image Imagery Imagery, Multispectral Processing, Remote Sensing, Supervised algorithm, algorithms, classification, classifier, computing, evolutionary extraction, feature genetic geophysical geophysics hybrid image infrared, land mapping, measurement multidimensional multispectral processing, programming, remote sensing, signal supervised surface, technique, techniques, terrain visible %N 2 %P 393--404 %T Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction %U http://citeseer.ist.psu.edu/561309.html %V 40 %X We have developed an automated feature detection/ classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing pipelines for a variety of feature detection/ classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.

@article{oai:CiteSeerPSU:561309, abstract = {We have developed an automated feature detection/ classification system, called Genie (GENetic Imagery Exploitation), which has been designed to generate image processing pipelines for a variety of feature detection/ classification tasks. Genie is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multi-spectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of Genie with several conventional supervised classification techniques, for a number of classification tasks using multi-spectral remotely-sensed imagery.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Harvey, Neal R. and Theiler, James and Brumby, Steven P. and Perkins, Simon and Szymanski, John J. and Bloch, Jeffrey J. and Porter, Reid B. and Galassi, Mark and Young, A. Cody}, biburl = {https://www.bibsonomy.org/bibtex/236a71f18103ea823d110ba9e17e72afb/brazovayeye}, interhash = {be943cee1ee34621f5314896a27d8a9e}, intrahash = {36a71f18103ea823d110ba9e17e72afb}, issn = {0196-2892}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {Algorithms, Classification, Evolutionary Exploitation, GENIE, GENetic IR, Image Imagery Imagery, Multispectral Processing, Remote Sensing, Supervised algorithm, algorithms, classification, classifier, computing, evolutionary extraction, feature genetic geophysical geophysics hybrid image infrared, land mapping, measurement multidimensional multispectral processing, programming, remote sensing, signal supervised surface, technique, techniques, terrain visible}, month = {February}, notes = {On line version not identical to IEEE version Inspec Accession Number: 7265352, CODEN: IGRSD2}, number = 2, pages = {393--404}, size = {12 pages}, timestamp = {2008-06-19T17:41:08.000+0200}, title = {Comparison of {GENIE} and conventional supervised classifiers for multispectral image feature extraction}, url = {http://citeseer.ist.psu.edu/561309.html}, volume = 40, year = 2002 }