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Automatic target recognition by means of polarimetric ISAR images and neural networks

IEEE Transactions on Geoscience and Remote Sensing, ISSN: 0196-2892, Vol: 47, Issue: 11, Page: 3786-3794
2009
  • 80
    Citations
  • 0
    Usage
  • 17
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    80
    • Citation Indexes
      80
  • Captures
    17

Article Description

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognizing targets. Moreover, the use of fully polarimetric ISAR (Pol-ISAR) images enhances classification capabilities. In this paper, the authors propose a novel automatic target recognition (ATR) technique based on the use of fully Pol-ISAR images and neural networks (NNs). In order to reduce the amount of data processed by the classifier, the brightest scattering centers are first extracted by means of the Pol-CLEAN technique, and then, their scattering matrices are decomposed using Cameron's decomposition. A classifier based on the use of multilayer perceptron NN that makes use of the features extracted from the Pol-ISAR images is then implemented. A proof-of-concept test is performed on real data acquired during a controlled experiment in an anechoic chamber. © 2006 IEEE.

Bibliographic Details

Marco Martorella; Elisa Giusti; Amerigo Capria; Fabrizio Berizzi; Bevan Bates

Institute of Electrical and Electronics Engineers (IEEE)

Engineering; Earth and Planetary Sciences

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