Summary
We present our most recent efforts in applying XCS to automatic target recognition (ATR). We place particular emphasis on ATR as a series of linked problems, which include pre-processing of multi-spectral data, detection of objects (in this case, vehicles) in that data, and identification (classification) of those objects. Multi-spectral data contains visual imagery, and additional imagery from several infrared spectral bands. The performance of XCS, with robust features, notably exceeds that of a template-based classifier on the pre-processed multi-spectral data for vehicle identification. Promising preliminary results are also presented for vehicle detection. Future directions for this research are discussed in the conclusions.
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Gandhe, A., Yu, SH., Mehra, R., Smith, R.E. (2008). XCS for Fusing Multi-Spectral Data in Automatic Target Recognition. In: Bull, L., Bernadó-Mansilla, E., Holmes, J. (eds) Learning Classifier Systems in Data Mining. Studies in Computational Intelligence, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78979-6_7
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DOI: https://doi.org/10.1007/978-3-540-78979-6_7
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