Detection and Classification of Buried Radioactive Metal Objects Using Wideband EMI Data
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Transcript of Detection and Classification of Buried Radioactive Metal Objects Using Wideband EMI Data
Electrical & Computer Engineering
Anish Turlapaty, Jenny Du, and Nicolas YounanDepartment of Electrical and Computer Engineering
Mississippi State University
IGARSS 2011 Vancouver, Canada
Detection and Classification of Buried Radioactive Metal Objects
Using Wideband EMI Data
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Outline• Overview and Background• Detection Methods• Methodology
– Feature Extraction– Multistage Learning– Validation
• Performance• Summary
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Overview
EMI Spectroscopy
• Object exposed to EM Field • Generates secondary magnetic field • Measure its spectrum • Identification of the target’s finger print • Classify the EMI Response• Validate the methodology by measuring correlation
against a library of signatures from lab experiments
Goal: Identify the target (Depleted Uranium DU)
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Background• Depleted Uranium (DU), in general, is considered both a
toxic and radioactive hazard - Effectively detecting DU is of great importance
• Although DU and other radioactive materials have different characteristics, the detection of DU from other metals is challenging due to spectrum similarities
• In practice, the situation can be much more complicated due to the presence of background clutters, especially when the DU is buried
• Even more challenging to accomplish detection in an automated fashion
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Detection Methods• Common techniques applicable to landmine
detection– Use a library of signatures– Bayesian approach– Bivariate Gaussian model
• However the problem of variable orientation of the target object is not solved
Utilizing EMI data, a pattern recognition approach based on a decision tree for DU detection is developed
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Validation
Feature Extraction
Multi-stage Learning
Signature Extraction
Field dataFeature Vectors
Classification Map
Best Fit
Target Signature
Methodology
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Field Data DescriptionField Data (raw test
data)Collected from a
rectangular grid of size 60m x 16m
EMIR is collected for 7 frequencies (widely separated)
330 990 3030 6030 13050 21300 43080
71 to
Classes 1- DU at surface, 2- DU at 30cm, 3- DU at 60cm, 4- clutter
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MotivationQuadratic component of EMI response of 1 inch DU rods has a peak at around 3.5kHz The relation between the peak value and the values from its neighboring bands can be very useful for characterizing DU metal of the same radii.
Feature Selection Relevant features: Four spectral values of quadratic component centered around the peak value in the region of interest are
ω1
ω2
ω3 ω4
ω5
ω6
ω75432 ,,,
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Central Features >
thr1
Central Features > other Features Background
Other Metals
Feature Vectors
YESNO
YESNO
Feature Subset
One-Class SVM Training
Map Generation
PDF Estimation
Clustering
Class MapClass
Separation
PDF Visualization
Multi-stage ClassificationCentral features are the spectral values at 43 ,
Threshold thr1 is determined from the histogram of the corresponding feature
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PDF Estimation
• The PDF has two distinct Gaussian peaks • The Gaussian peaks correspond to the centers of two Gaussian
distributions, thus two clusters
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Clustering
• Individual feature vectors are classified to one of these two clusters based on their distance to the two centers
• A threshold value is used to reject vectors that do not belong to either cluster
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Classification Map
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Validation
Linear Model
Laboratory Measurements with
GEM-3 sensor
reftest QaQ
Reference SignaturesBest fit
Target Response
EMI Response is measured for seven metal cylindrical rods of 4inch length and 1inch diameter at 29 frequencies from 90 Hz to 90KHz.
Target response is basically quadratic response of objects at selected locations from classification map
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Validation Contd.• The Quadratic component of the EMI
Response of the buried target should have high correlation with the EMI response of the same object measured in the laboratory.
• Depth vs. Magnitude of EMI Response for compact objects
• The magnitude of H field inversely depends on Nth power of the distance
• Thus objects of class 1 have higher magnitude as they are closer to surface
• Class 2 objects are much deeper thus relatively weaker signal strength
fQuadafQuad labfield
}){,,,(1secondary jYXzyxafH
Corresponds to Quadrature response of DU objects at the surface (Class 1)
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Mean EMI ResponseCorresponds to DU objects at 30cm depthSupported by the reduced magnitude of the EMI response
Non-DU metal object with different EMI signature (clutter)
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Performance
• Multistage approach with SVM for DU discrimination and depth separation
• Confusion Matrix
Predicted ClassesActual Classes
1(DU) 2(Clutter) 3(soil)
1(DU) 261 0 292(Clutter) 12 89 03(Soil) 0 0 8158
DU ObjectsClutter
Soil Background
Average accuracy 95 %
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Summary
• Unsupervised classification of different objects at different depths using a multistage learning method with OCSVMs is shown to be quite successful
• This method is validated by comparing the target signatures from each class with laboratory measurements
• Extension of this work is testing the detection and/or discrimination algorithm in the presence of substantial clutter and variable size DU objects