Evolving spatio-spectral feature extraction algorithms for hyperspectral imagery

Steven P. Brumby; Amy E. Galbraith

doi:10.1117/12.451692

8 November 2002 Evolving spatio-spectral feature extraction algorithms for hyperspectral imagery

Steven P. Brumby, Amy E. Galbraith

Proceedings Volume 4816, Imaging Spectrometry VIII; (2002) https://doi.org/10.1117/12.451692
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

Hyperspectral imagery data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problem of dealing with the sheer amount of spectral information per pixel in a hyperspectral image, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. Rather than carry out this algorithm exploration by hand, we are interested in developing learning systems that can evolve these algorithms. We describe a genetic programming/supervised classifier software system, called GENIE, which evolves image processing tools for remotely sensed imagery. Our primary application has been land-cover classification from satellite imagery. GENIE was developed to evolve classification algorithms for multispectral imagery, and the extension to hyperspectral imagery presents a chance to test a genetic programming system by greatly increasing the complexity of the data under analysis, as well as a chance to find interesting spatio-spectral algorithms for hyperspectral imagery. We demonstrate our system on publicly available imagery from the new Hyperion imaging spectrometer onboard the NASA Earth Observing-1 (EO-1) satellite.

Citation Download Citation

Steven P. Brumby and Amy E. Galbraith "Evolving spatio-spectral feature extraction algorithms for hyperspectral imagery", Proc. SPIE 4816, Imaging Spectrometry VIII, (8 November 2002); https://doi.org/10.1117/12.451692

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