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Proceedings Paper

Feature space trajectory (FST) neural network for SAR detection, classification, and clutter rejection
Author(s): David P. Casasent; Rajesh Shenoy; Leonard Neiberg
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Paper Abstract

We consider use of eigenvector feature inputs to our feature space trajectory (FST) neural net classifier for SAR data with 3D aspect distortions. We consider its use for classification and pose estimation and rejection of clutter. Prior and new MINACE distortion-invariant and shift- invariant filter work to locate the position of objects in regions of interest is reviewed. Test results on a number of SAR databases are included to show the robustness of the algorithm. New results include techniques to determine: the number of eigenvectors per class to retain, the number and order of final features to use, if the training set size is adequate, and if the training and test sets are compatible.

Paper Details

Date Published: 10 June 1996
PDF: 7 pages
Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); doi: 10.1117/12.242038
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Rajesh Shenoy, Carnegie Mellon Univ. (United States)
Leonard Neiberg, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2757:
Algorithms for Synthetic Aperture Radar Imagery III
Edmund G. Zelnio; Robert J. Douglass, Editor(s)

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