Subsurface discrimination using electromagnetic induction sensors

Bell, Thomas H.; Barrow, Bruce; Miller, James D.

doi:10.1109/36.927451

Cited by 180 publications

(117 citation statements)

References 3 publications

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“…As shown in (19), the optimal classifier operates on the entire signal y. It is known that if the model parameters (such as poles in our case) are sufficient statistics [34], then the optimal classification can be done in a lower dimensional feature space.…”

Section: B Optimal Classificationmentioning

confidence: 99%

“…Although the Monte Carlo methods can be used to evaluate (19), they are usually quite time consuming. Therefore, we investigate other solutions in this paper.…”

Section: B Optimal Classificationmentioning

confidence: 99%

“…For example, it is a standard practice to subtract the response of the background in the absence of the target from the data [14], [19]. Additional noise deriving, for example, from sensor position uncertainty and mutual coupling in the presence of other metallic targets must also be considered.…”

Section: Processing Model and Prior Workmentioning

confidence: 99%

“…The Laplacian approximation [36] is used to approximate the posterior distribution in (19). It is based on the asymptotic Gaussian approximation of the likelihood in (19) and can be written as…”

Section: ) Laplacian Approximated Classifier (La-glrt)mentioning

confidence: 99%

“…It is based on the asymptotic Gaussian approximation of the likelihood in (19) and can be written as…”

Section: ) Laplacian Approximated Classifier (La-glrt)mentioning

confidence: 99%

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Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models

Aliamiri¹,

Stalnaker

Miller³

2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-We used kernel density estimation (KDE) methods to build a priori probability density functions (pdfs) for the vector of features that are used to classify unexploded ordnance items given electromagnetic-induction sensor data. This a priori information is then used to develop a new suite of estimation and classification algorithms. As opposed to the commonly used maximumlikelihood parameter estimation methods, here we employ a maximum a posteriori (MAP) estimation algorithm that makes use of KDE-generated pdfs. Similarly, we use KDE priors to develop a suite of classification schemes operating in both "feature" space as well as "signal/data" space. In terms of feature-based methods, we construct a support vector machine classifier and its extension to support M -ary classification. The KDE pdfs are also used to synthesize a MAP feature-based classifier. To address the numerical challenges associated with the optimal data-space Bayesian classifier, we have used several approximation techniques, including Laplacian approximation and generalized likelihood ratio tests employing the priors. Using both simulations and real field data, we observe a significant improvement in classification performance due to the use of the KDE-based prior models.

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Section: B Optimal Classificationmentioning

confidence: 99%

“…Although the Monte Carlo methods can be used to evaluate (19), they are usually quite time consuming. Therefore, we investigate other solutions in this paper.…”

Section: B Optimal Classificationmentioning

confidence: 99%

Section: Processing Model and Prior Workmentioning

confidence: 99%

Section: ) Laplacian Approximated Classifier (La-glrt)mentioning

confidence: 99%

“…It is based on the asymptotic Gaussian approximation of the likelihood in (19) and can be written as…”

Section: ) Laplacian Approximated Classifier (La-glrt)mentioning

confidence: 99%

See 3 more Smart Citations

Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models

Aliamiri¹,

Stalnaker

Miller³

2007

IEEE Trans. Geosci. Remote Sensing

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Buried Object Detection

Türk¹,

Hocaoglu²

2005

Encyclopedia of RF and Microwave Engineering

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The subsurface detection and identification of the buried geologic and synthetic (human‐produced) structures is currently an important research area. The scope of the problem is very complex and covers a wide range in applications. One area of the research includes the detection of a large metallic gas pipe buried in a deep or small plastic mine laid just beneath the ground. The size, geometry, constitution and depth of the target object and the characteristics of the medium (wet soil, sand, etc.) can be considered as the principal parameters for the detector designs. In this article, different sensor technologies based mainly on electromagnetic, acoustics, infrared, or chemical theories are discussed.

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