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Proceedings Paper

Truncated feature representation for automatic target detection using transformed data-based decomposition
Author(s): Vahid R. Riasati
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Paper Abstract

In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

Paper Details

Date Published: 12 May 2016
PDF: 14 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440V (12 May 2016); doi: 10.1117/12.2228752
Show Author Affiliations
Vahid R. Riasati, Raytheon Space & Airborne Systems (United States)

Published in SPIE Proceedings Vol. 9844:
Automatic Target Recognition XXVI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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