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9 March 1999 Eigenspace transformation for automatic target recognition
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lipchen Alex Chan, Nasser M. Nasrabadi, and Don Torrieri "Eigenspace transformation for automatic target recognition", Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999);

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