By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yan Zhang ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Xuejun Liao ; L. Carin

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 {xi,yi}i=1,N, where xi is the measured data for buried object i, and yi is the associated unknown binary label (target/nontarget). Let the N xi 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 Bn⊆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 Xs⊆X, composed of those xi for which knowledge of the associated labels yi would be most informative in defining the weights for the basis functions in Bn; 3) the buried objects associated with the signatures in Xs are excavated, yielding the associated labels yi, represented by the set Ys; and 4) using Bn,Xs, and Ys, 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.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:42 ,  Issue: 11 )