Active selection of labeled data for target detection

An information-theoretic approach is developed for target detection, with active training set selection, directly from the site-specific measured data. For the proposed kernel-based algorithm, a set of basis functions to characterize the signature distribution of the site are defined first; then we determine a parsimonious set of data, for which knowledge of the associated labels would be most informative to determine the weights for the basis functions. Both of them utilize the Fisher information criteria. The proposed framework is applied to subsurface target detection, with example results presented for an actual buried unexploded ordnance site.