Proceedings of SPIE, Volume 4726, AeroSense, Orlando, FL (April 2002)
Numerous feature detectors have been defined for detecting military vehicles in natural scenes. These features can be computed for a given image chip containing a known target and used to train a classifier. This classifier can then be used to assign a label to an un-labeled image chip. The performance of the classifier is dependent on the quality of the set of features used. In this paper, we first describe a set of features commonly used by the Automatic Target Recognition (ATR) community. We then analyze feature performance on a vehicle identification task in laser radar (LADAR) imagery. Our features are computed over both the range and reflectance channels. In addition, we perform feature subset selection using two different methods and compare the results. The goal of this analysis is to determine which subset of features to choose in order to optimize performance in LADAR Autonomous Target Acquisition (ATA).
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