Landmine detection with Bayesian cross-categorization on point-wise, contextual and spatial features

Jasmin Léveillé; Ssu-Hsin Yu; Avinash Gandhe

doi:10.1117/12.2224327

3 May 2016 Landmine detection with Bayesian cross-categorization on point-wise, contextual and spatial features

Jasmin Léveillé, Ssu-Hsin Yu, Avinash Gandhe

Proceedings Volume 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI; 982307 (2016) https://doi.org/10.1117/12.2224327
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Recently developed feature extraction methods proposed in the explosive hazard detection community have yielded many features that potentially provide complementary information for explosive detection. Finding the right combination of features that is most effective in distinguishing targets from clutter, on the other hand, is extremely challenging due to a large number of potential features to explore. Furthermore, sensors employed for mine and buried explosive hazard detection are typically sensitive to environmental conditions such as soil properties and weather as well as other operating parameters. In this work, we applied Bayesian cross-categorization (CrossCat) to a heterogeneous set of features derived from electromagnetic induction (EMI) sensor time-series for purposes of buried explosive hazard detection. The set of features used here includes simple, point-wise measurements such as the overall magnitude of the EMI response, contextual information such as soil type, and a new feature consisting of spatially aggregated Discrete Spectra of Relaxation Frequencies (DSRFs). Previous work showed that the DSRF characterizes target properties with some invariance to orientation and position. We have developed a novel approach to aggregate point-wise DSRF estimates. The spatial aggregation is based on the Bag-of-Words (BoW) model found in the machine learning and computer vision literatures and aims to enhance the invariance properties of point-wise DSRF estimates. We considered various refinements to the BoW model for purpose of buried explosive hazard detection and tested their usefulness as part of a Bayesian cross-categorization framework on data collected from two different sites. The results show improved performance over classifiers using only point-wise features.

Citation Download Citation

Jasmin Léveillé, Ssu-Hsin Yu, and Avinash Gandhe "Landmine detection with Bayesian cross-categorization on point-wise, contextual and spatial features", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 982307 (3 May 2016); https://doi.org/10.1117/12.2224327

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KEYWORDS

Electromagnetic coupling

Explosives

Explosives detection

Feature selection

Land mines

Sensors

Environmental sensing

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