Hyperspectral Image Analysis Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Brian J. Ross and Anthony G. Gualtieri and 
                 Frank Fueten and Paul Budkewitsch",
  title =        "Hyperspectral Image Analysis Using Genetic
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and 
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and 
                 V. Honavar and G. Rudolph and J. Wegener and 
                 L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and 
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "1196--1203",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming, real world
                 applications, hyperspectral imaging, mineral
  ISBN =         "1-55860-878-8",
  URL =          "http://www.cosc.brocku.ca/~bross/research/RWA008.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA008.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
  URL =          "http://citeseer.ist.psu.edu/503556.html",
  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).",
  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

Genetic Programming entries for Brian J Ross Anthony G Gualtieri Frank Fueten Paul Budkewitsch