Independent component analysis for UXO detection in highly cluttered environments

Throckmorton, Chandra S.; Tantum, Stacy L.; Tan, Yingyi; Collins, Leslie M.

doi:10.1016/j.jappgeo.2006.05.007

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Recently, PCA has been applied to airborne EM (AEM) surveys to construct intuitive RGB maps based on the principal components (Green, 1998). In other electromagnetic applications, Asten (2009) has used PCA in unexploded ordnance TEM surveys to classify targets, while , , and Throckmorton et al (2007) have used similar techniques using higher-order moments (Independent Component Analysis) to recover diagnostic parameters of differing ordnance items. The most common use of Principal Component Analysis in EM is to combine magnetic and electromagnetic datasets (e.g.…”

Section: Principal Component Analysismentioning

confidence: 99%

Enhancement of TEM Data and Noise Characterization by Principal Component Analysis

Kass¹,

Li²,

Krahenbuhl³

et al. 2010

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. The research performed over the life-span of this project has progressed satisfactorily with the major research tasks completed approximately to project plan. In addition, we have identified an additional area of research critical to the application of PCA to TEM data and added these aspects to the research plan. REPORT DATE MAY 20102First, we developed a principal component analysis algorithm tailored to unexploded ordnance applications. Decay characteristics of TEM data preclude the standard Karhunen-Loéve transform; we have addressed these issues with algorithm modifications and incorporated these into the workflow.Secondly, we identified the optimum choice of principal components for the attenuation of both random noise and correlated noise, leaving the signal due to UXO intact. We show that the processed data is optimally prepared for automatic anomaly picking routines with a highly reduced number of false anomalies. We demonstrate this on both synthetic examples of UXO surveys, as well as on TEM data from Kaho'olawe, Hawaii, USA.iii Finally, we have identified a critical issue with inversion of processed data that results in extremely inaccurate recovered models without the incorporation of the PCA process into the forward model. We developed an inversion algorithm which takes the processing steps into account during construction of the inverse kernels.This leads to more accurate recovered models of inverted anomalies.iv The goal is to increase the signal-to-noise ratio of the TEM data as well as to generate an estimate of data noise characteristics (statistical distribution and associated parameters) for use in subsequent inversion-based discrimination.One of the most effective geophysical techniques for UXO application is the transient electromagnetic method. Using a loop as a source, a time-varying magnetic field is generated at the surface which in turn induces electrical currents in the ground, and more importantly in buried metallic objects. Once induced, these currents dissipate over time due to ohmic losses, leading to a measured transient decay in magnetic field flux density at the surface by geophysical sensors. The currents induced in the surrounding medium and in the buried metallic ob...

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Section: Principal Component Analysismentioning

confidence: 99%

Enhancement of TEM Data and Noise Characterization by Principal Component Analysis

Kass¹,

Li²,

Krahenbuhl³

et al. 2010

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“…A significant number of studies have been performed on the discrimination of targets. One approach is to analyse metal detector responses (e.g., Pasion et al, 2007;Shubitidze et al, 2007;Throckmorton et al, 2007). Another approach is to exploit other subsurface sensing techniques in addition to a metal detector or as a standalone detector.…”

Section: Introductionmentioning

confidence: 99%

Soil properties and performance of landmine detection by metal detector and ground-penetrating radar — Soil characterisation and its verification by a field test

Takahashi

Preetz

Igel

2011

Journal of Applied Geophysics

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“…Response from shallow yet small metallic clutter can often obscure deeper targets of interest (for example, see Figs. [28][29][30], and the electromagnetic induction (EMI) response from targets in close proximity may affect each other due to mutual coupling [3,4] and confuse or confound inversion schemes. These realities often lead to misclassification at best, or a complete failure of the routine at worst.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies cast the problem as a blind source separation (BSS) problem [5][6][7]. These studies assumed no interaction between anomalies which interaction is known to exist [8], were constrained to a maximum of two targets [5], were simulations that had prior knowledge of the target [6], or a combination of similar limitations. Other studies used data from multiple instruments (such as simultaneous EMI and GPR) which can be costly and is not generally available [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, an independent component analysis revealed the possibility of extracting the signature of a UXO in the presence of multiple clutter items, but the method concentrated on the specific situation of known UXO, i.e. a known signature which had to be extracted from a sum of multiple signatures [30]. More recently, positions, orientations, and time dependent polarizabilities were inverted on two-target configurations from both synthetic as well as sensor data [31].…”

Section: 2a Introductionmentioning

confidence: 99%

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Isolating and Discriminating Overlapping Signatures in Cluttered Environments

Barrowes¹,

Shubitidze²

2011

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Independent component analysis for UXO detection in highly cluttered environments

Cited by 13 publications

References 25 publications

Enhancement of TEM Data and Noise Characterization by Principal Component Analysis

Enhancement of TEM Data and Noise Characterization by Principal Component Analysis

Soil properties and performance of landmine detection by metal detector and ground-penetrating radar — Soil characterisation and its verification by a field test

Isolating and Discriminating Overlapping Signatures in Cluttered Environments

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