Multiple instance learning framework for landmine detection using ground penetrating radar

Ground Penetrating Radar (GPR) data provides a powerful technique to identify subsurface buried threats. Although GPR data contains a three-dimensional representation of the subsurface, object truth (i.e. labels and positions of true threat objects in training lanes) is often provided in only two dimensions (GPS coordinates along the earth's surface). To mitigate uncertainty in an object's location in depth, many successful feature extraction/ object recognition techniques in GPR extract feature vectors from several depth regions, and attempt to combine information across these feature vectors to make final decisions. However, many machine learning techniques are not well suited for learning under these conditions. Multiple Instance Learning (MIL) is a type of supervised learning method in which labels are available for sets of samples, but not for individual samples. The goal of learning in MIL is to classify new sets of samples as they become available. This set-based framework is useful in processing GPR responses since features are often extracted independently from multiple un-labeled depth bins, and thus a set of features is produced at each potential threat location. In this work, a comparison of several previous approaches to MlL applied to landmine detection in GPR data is presented. One recent algorithm, the p-Posterior Mixture Model approach (pPMM) is given special attention, and several slight modifications to the pPMM approach are presented and compared. © 2011 SPIE.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering