Selecting a Discrimination Algorithm for Unexploded Ordnance Remediation

We review the algorithms that have been used to discriminate between hazardous unexploded ordnance (UXO) and harmless clutter. Statistical classifiers use model parameters estimated from geophysical data to formulate a decision rule. This rule tries to discriminate between UXO and clutter using the available information. In contrast, library-based discrimination algorithms make decisions using a predefined library of signatures for expected UXO types. Given the variety of algorithms that are available for UXO discrimination, we describe two metrics for evaluating discrimination performance - the area under the receiver operating characteristic and the false-alarm rate. We propose a bootstrapping algorithm for estimating these metrics when limited data are available. Last, we demonstrate this approach on real electromagnetic and magnetic data sets.