Comparison of supervised and unsupervised machine learning techniques for UXO classification using EMI data

Abstract

Classification tools including Support Vector Machines (SVM) and Neural Networks (NN) are employed, and their performances compared for Unexploded Ordnance (UXO) classification using live site electromagnetic induction (EMI) data. Both SVM and NN are examples of supervised machine-learning techniques, whose purpose is to label the features (extracted from the incoming data of the unknown subsurface anomalies) based on previously trained examples. In this paper a set of three features are extracted from the EMI decay curves of the physics-based intrinsic, effective dipole moment, called the total Normalized Surface Magnetic Source (NSMS). This data is first used to train both the SVM and NN models and further serves as a basis for UXO classification. These techniques are then compared to an unsupervised learning approach, based on agglomerative hierarchical clustering followed by Gaussian Mixture modeling. We found that such combination provides reduction in the amount of required training data (which is being requested solely based on the clustering results) and allows for convenient probabilistic interpretation of the classification. The classification results themselves depend on the UXO caliber, material composition and actual live UXO site's conditions. Therefore, here we report the classification results for a live UXO data set, collected at former Camp San Luis Obispo, CA. This study includes four targets-of-interest: 60-mm, 81-mm, and 4.2-in mortars and 2.36-in rockets. The classification performance between clutters and UXO is studied and the corresponding ROC curves are illustrated.