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Proceedings Paper

Multiclass SAR feature space trajectory (FST) neural network class and pose estimation results
Author(s): Rajesh Shenoy; David P. Casasent
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Paper 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.

Paper Details

Date Published: 28 July 1997
PDF: 4 pages
Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); doi: 10.1117/12.281549
Show Author Affiliations
Rajesh Shenoy, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3070:
Algorithms for Synthetic Aperture Radar Imagery IV
Edmund G. Zelnio, Editor(s)

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