Evolving land cover classification algorithms for multispectral and multitemporal imagery

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Steven P. Brumby and James Theiler and 
                 Jeffrey J. Bloch and Neal R. Harvey and Simon Perkins and 
                 John J. Szymanski and A. Cody Young",
  title =        "Evolving land cover classification algorithms for
                 multispectral and multitemporal imagery",
  booktitle =    "Proc. SPIE Imaging Spectrometry VII",
  year =         "2002",
  editor =       "Michael R. Descour and Sylvia S. Shen",
  volume =       "4480",
  pages =        "120--129",
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, Feature
                 Extraction, Supervised classification, K-means
                 clustering, Multi-spectral imagery, Land cover,
  URL =          "http://public.lanl.gov/jt/Papers/brumby_SPIE4480-14.pdf",
  URL =          "http://citeseer.ist.psu.edu/445835.html",
  DOI =          "doi:10.1117/12.453331",
  size =         "10 pages",
  abstract =     "The Cerro Grande/Los Alamos forest fire devastated
                 over 43,000 acres (17,500 ha) of forested land, and
                 destroyed over 200 structures in the town of Los Alamos
                 and the adjoining Los Alamos National Laboratory. The
                 need to measure the continuing impact of the fire on
                 the local environment has led to the application of a
                 number of remote sensing technologies. During and after
                 the fire, remote-sensing data was acquired from a
                 variety of aircraft- and satellite-based sensors,
                 including Landsat 7 Enhanced Thematic Mapper (ETM+). We
                 now report on the application of a machine learning
                 technique to the automated classification of land cover
                 using multi-spectral and multi-temporal imagery.

                 We apply a hybrid genetic programming/supervised
                 classification technique to evolve automatic feature
                 extraction algorithms. We use a software package we
                 have developed at Los Alamos National Laboratory,
                 called GENIE, to carry out this evolution. We use
                 multispectral imagery from the Landsat 7 ETM+
                 instrument from before, during, and after the wildfire.
                 Using an existing land cover classification based on a
                 1992 Landsat 5 TM scene for our training data, we
                 evolve algorithms that distinguish a range of land
                 cover categories, and an algorithm to mask out clouds
                 and cloud shadows. We report preliminary results of
                 combining individual classification results using a
                 K-means clustering approach. The details of our evolved
                 classification are compared to the manually produced
                 land-cover classification.",
  notes =        "Los Alamos National Lab.",

Genetic Programming entries for Steven P Brumby James Theiler Jeffrey J Bloch Neal R Harvey Simon Perkins John J Szymanski A Cody Young