Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

@article{Harvey2002ComparisonOG,
  title={Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction},
  author={Neal R. Harvey and James Theiler and Steven P. Brumby and Simon Perkins and John J. Szymanski and Jeffrey J. Bloch and Reid B. Porter and Mark Galassi and A. Cody Young},
  journal={IEEE Trans. Geosci. Remote. Sens.},
  year={2002},
  volume={40},
  pages={393-404}
}
The authors have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. The authors describe their system in detail together with experiments involving comparisons of GENIE… 
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