Context-dependent feature selection using unsupervised contexts applied to GPR-based landmine detection

Christopher R. Ratto; Peter A. Torrione; Leslie M. Collins

doi:10.1117/12.850906

29 April 2010 Context-dependent feature selection using unsupervised contexts applied to GPR-based landmine detection

Christopher R. Ratto, Peter A. Torrione, Leslie M. Collins

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 76642I (2010) https://doi.org/10.1117/12.850906
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Context-dependent classification techniques applied to landmine detection with ground-penetrating radar (GPR) have demonstrated substantial performance improvements over conventional classification algorithms. Context-dependent algorithms compute a decision statistic by integrating over uncertainty in the unknown, but probabilistically inferable, context of the observation. When applied to GPR, contexts may be defined by differences in electromagnetic properties of the subsurface environment, which are due to discrepancies in soil composition, moisture levels, and surface texture. Context-dependent Feature Selection (CDFS) is a technique developed for selecting a unique subset of features for classifying landmines from clutter in different environmental contexts. In past work, context definitions were assumed to be soil moisture conditions which were known during training. However, knowledge of environmental conditions could be difficult to obtain in the field. In this paper, we utilize an unsupervised learning algorithm for defining contexts which are unknown a priori. Our method performs unsupervised context identification based on similarities in physics-based and statistical features that characterize the subsurface environment of the raw GPR data. Results indicate that utilizing this contextual information improves classification performance, and provides performance improvements over non-context-dependent approaches. Implications for on-line context identification will be suggested as a possible avenue for future work.

Citation Download Citation

Christopher R. Ratto, Peter A. Torrione, and Leslie M. Collins "Context-dependent feature selection using unsupervised contexts applied to GPR-based landmine detection", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76642I (29 April 2010); https://doi.org/10.1117/12.850906

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