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Model-based target recognition in pulsed ladar imagery.

Q Zheng1, S Z Der, H I Mahmoud

  • 1Center for Automation Research, University of Maryland, College Park, MD 21201, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study presents a pulsed ladar system for object recognition and automatic target recognition (ATR). The system effectively uses synthetic data for training, demonstrating successful recognition of military vehicles in real-world ladar imagery.

Area of Science:

  • Engineering
  • Computer Science
  • Physics

Background:

  • Automatic target recognition (ATR) systems are crucial for defense applications.
  • Ladar (laser detection and ranging) technology offers robust sensing capabilities for object identification.
  • Training ATR systems often requires extensive and diverse datasets, which can be challenging to acquire.

Purpose of the Study:

  • To develop and evaluate a pulsed ladar-based object recognition system for ATR.
  • To investigate the efficacy of using simulated synthetic data for training ladar-based recognizers.
  • To demonstrate the system's performance on real-world ladar imagery of military vehicles.

Main Methods:

  • A pulsed ladar system was employed for sensing.
  • Range images were fitted to templates generated via laser physics simulation of geometric target models.

Related Experiment Videos

  • A projection-based prescreener was used to filter candidate templates, achieving over 80% reduction.
  • Object recognition combined M-of-N pixel matching for internal shape analysis with silhouette matching.

    • Main Results:

    • The system was trained using synthetic ladar data generated from simulations.
    • Blind testing was conducted on a dataset of real ladar images featuring military vehicles at various orientations and ranges.
    • The system demonstrated successful recognition capabilities on the real imagery, validating the training approach.

    Conclusions:

    • Synthetic data generated through laser physics simulations is effective for training ladar-based object recognition systems.
    • The developed system shows practical utility for automatic target recognition applications using ladar imagery.
    • The combination of simulation-based training and robust matching algorithms enables high performance in real-world scenarios.