Truncated feature representation for automatic target detection using transformed data-based decomposition

Vahid R. Riasati

doi:10.1117/12.2228752

12 May 2016 Truncated feature representation for automatic target detection using transformed data-based decomposition

Vahid R. Riasati

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440V (2016) https://doi.org/10.1117/12.2228752
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Vahid R. Riasati "Truncated feature representation for automatic target detection using transformed data-based decomposition", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440V (12 May 2016); https://doi.org/10.1117/12.2228752

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