Detection of buried targets via active selection of labeled data: application to sensing subsurface UXO

When sensing subsurface targets, such as landmines and unexploded ordnance (UXO), the target signatures are typically a strong function of environmental and historical circumstances. Consequently, it is difficult to constitute a universal training set for design of detection or classification algorithms. In this paper, we develop an efficient procedure by which information-theoretic concepts are used to design the basis functions and training set, directly from the site-specific measured data. Specifically, assume that measured data (e.g., induction and/or magnetometer) are available from a given site, unlabeled in the sense that it is not known a priori whether a given signature is associated with a target or clutter. For N signatures, the data may be expressed as {x/sub i/,y/sub i/}/sub i=1,N/, where x/sub i/ is the measured data for buried object i, and y/sub i/ is the associated unknown binary label (target/nontarget). Let the N x/sub i/ define the set X. The algorithm works in four steps: 1) the Fisher information matrix is used to select a set of basis functions for the kernel-based algorithm, this step defining a set of n signatures B/sub n//spl sube/X that are most informative in characterizing the signature distribution of the site; 2) the Fisher information matrix is used again to define a small subset X/sub s//spl sube/X, composed of those x/sub i/ for which knowledge of the associated labels y/sub i/ would be most informative in defining the weights for the basis functions in B/sub n/; 3) the buried objects associated with the signatures in X/sub s/ are excavated, yielding the associated labels y/sub i/, represented by the set Y/sub s/; and 4) using B/sub n/,X/sub s/, and Y/sub s/, a kernel-based classifier is designed for use in classifying all remaining buried objects. This framework is discussed in detail, with example results presented for an actual buried-UXO site.