Classification of Buried Targets Using Ground Penetrating Radar: Comparison Between Genetic Programming and Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Kobashigawa:2011:LAWP,
  author =       "Jill S. Kobashigawa and Hyoung-sun Youn and 
                 Magdy F. Iskander and Zhengqing Yun",
  title =        "Classification of Buried Targets Using Ground
                 Penetrating Radar: Comparison Between Genetic
                 Programming and Neural Networks",
  journal =      "IEEE Antennas and Wireless Propagation Letters",
  year =         "2011",
  volume =       "10",
  pages =        "971--974",
  size =         "4 pages",
  abstract =     "The detection and classification of buried targets
                 such as unexploded ordnance (UXO) using ground
                 penetrating radar (GPR) technology involves complex
                 qualitative features and 2-D scattering images. These
                 processes are often performed by human operators and
                 are thus subject to error and bias. Artificial
                 intelligence (AI) technologies, such as neural networks
                 (NN) and fuzzy systems, have been applied to develop
                 autonomous classification algorithms and have shown
                 promising results. Genetic programming (GP), a
                 relatively new AI method, has also been examined for
                 these classification purposes. In this letter, the
                 results of a comparison between the classification
                 performances of NN versus the GP techniques for GPR UXO
                 data are presented. Simulated 2-D scattering patterns
                 from one UXO target and four non-UXO objects are used
                 in this comparison. Different levels of noise and cases
                 of untrained data are also examined. Obtained results
                 show that GP provides better performance than NN
                 methods with increasing problem difficulty. Genetic
                 programming also showed robustness to untrained data as
                 well as an inherent capability of providing global
                 optimal searching, which could minimise efforts on
                 training processes.",
  keywords =     "genetic algorithms, genetic programming, 2D image
                 scattering, AI technology, GP techniques, GPR UXO data,
                 NN techniques, artificial intelligence technology,
                 buried target classification, buried target detection,
                 fuzzy systems, global optimal searching, ground
                 penetrating radar technology, human operators, neural
                 networks, unexploded ordnance, artificial intelligence,
                 buried object detection, ground penetrating radar,
                 image classification, neural nets, pattern clustering,
                 radar computing, radar imaging, search problems",
  DOI =          "doi:10.1109/LAWP.2011.2167120",
  ISSN =         "1536-1225",
  notes =        "Also known as \cite{6009168}",
}

Genetic Programming entries for Jill S K Nakatsu Hyoung-sun Youn Magdy F Iskander Zhengqing Yun

Citations