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Proceedings Paper

Eigenspace transformation for automatic target recognition
Author(s): Lipchen Alex Chan; Nasser M. Nasrabadi; Don Torrieri
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Paper Abstract

In this paper, two eigenspace transformations are examined for feature extraction and dimensionality reduction in an automatic target detector. The transformations considered in this research are principal component analysis and the eigenspace separation transform (EST). These transformations differ in their capabilities to enhance the class separability and to compact the information for a given training set. The transformed data, obtained by projection of the normalized input images onto a chosen set of eigentargets, are fed to a multilayer perceptron (MLP) that decides whether a given input image is a target or clutter. In order to search for the optimal performance, we use different sets of eigentargets and construct the matching MLPs. Although the number of hidden layers is fixed, the numbers of inputs and weights of these MLPs are proportional to the number of eigentargets selected. These MLPs are trained with a modified Qprop algorithm that maximizes the target-clutter class separation at a predefined false-alarm rate. Experimental results are presented on a huge and realistic data set of forward-looking IR imagery.

Paper Details

Date Published: 9 March 1999
PDF: 12 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341106
Show Author Affiliations
Lipchen Alex Chan, U.S. Army Research Lab. (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Don Torrieri, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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