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Project Summary

University of Tennessee, in partnership with North Carolina State University, proposes multi-spectral and geometrical based automated target recognition - a research effort aimed at developing a "smart" target recognition system which takes advantage of both the multi-spectral and geometrical information available at different ranges from the target.

The innovation of this research effort is that it proposes an integrated approach to conduct ATR based on a weighted combination of the multispectral and geometric information, where the weight is adapted to the change of range between the target and the smart ordnance. Four tasks are proposed: 1) Develop a feature space using a multispectral snake technique which is both spectral- and geometric-based, 2) Develop classifier fusion algorithm that uses weights of evidence as a measure of the belief in different classifiers (either spectral- or geometric-based) in the context of Dempster's Rule of Combination, 3) Develop feature fusion algorithms based on a modified Hausdorff distance such that both spectral and spatial features are embedded in the process of template matching, and 4) Algorithm implementation and evaluation against test data in three forms, including synthetic data, calibrated data, and field data.

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