Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery (original) (raw)

Abstract

We use the genetic programming (GP) paradigm for two tasks. The first task given a GP is the generation of rules for the target / clutter classification of a set of synthetic aperture radar (SAR) images, the second, the generation of rules for the identification of tanks in a second set of SAR images. To perform these tasks, previously defined feature sets are generated on the various images, and GP is used to select relevant features and methods of analyzing these features. GP results are then compared with previous work using the feature sets.

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Authors and Affiliations

  1. Advanced Information Systems Group, ERIM International, P.O. Box 134008, 48113-4008, Ann Arbor, Michigan, USA
    Stephen A. Stanhope
  2. Artificial Intelligence Laboratory & Space Physics Research Laboratory, The University of Michigan, 2455 Hayward Avenue, 48109-2143, Ann Arbor, Michigan, USA
    Jason M. Daida

Authors

  1. Stephen A. Stanhope
  2. Jason M. Daida

Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Stanhope, S.A., Daida, J.M. (1998). Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040824

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