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Proceedings Article

Multiclass SAR feature space trajectory (FST) neural network class and pose estimation results

[+] Author Affiliations
Rajesh Shenoy, David P. Casasent

Carnegie Mellon Univ. (USA)

Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, 121 (July 28, 1997); doi:10.1117/12.281549
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From Conference Volume 3070

  • Algorithms for Synthetic Aperture Radar Imagery IV
  • Edmund G. Zelnio
  • Orlando, FL, USA | April 21, 1997

abstract

The feature space trajectory representation and neural network is used for classification and pose estimation of distorted objects in SAR data. New feature spaces and techniques to extend the concept to multiple classes are emphasized with initial four class results. On 4 class data, we obtain Pc equals 98.3 percent and clutter PFA equals 0.026/km2.

© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Rajesh Shenoy and David P. Casasent
"Multiclass SAR feature space trajectory (FST) neural network class and pose estimation results", Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, 121 (July 28, 1997); doi:10.1117/12.281549; http://dx.doi.org/10.1117/12.281549


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