Detection and Classification of Buried Radioactive Metal Objects Using Wideband EMI Data<br />AnishTurlapaty, Jenny Du, and Nicolas Younan<br />Department of Electrical and Computer Engineering<br />Mississippi State University<br />IGARSS 2011 Vancouver, Canada<br />
Background<br />Depleted Uranium (DU), in general, is considered both a toxic and radioactive hazard - Effectively detecting DU is of great importance<br />Although DU and other radioactive materials have different characteristics, the detection of DU from other metals is challenging due to spectrum similarities<br />In practice, the situation can be much more complicated due to the presence of background clutters, especially when the DU is buried<br />Even more challenging to accomplish detection in an automated fashion<br />
Detection Methods<br />Utilizing EMI data, a pattern recognition approach based on a decision tree for DU detection is developed<br />Common techniques applicable to landmine detection<br />Use a library of signatures<br />Bayesian approach<br />Bivariate Gaussian model<br />However the problem of variable orientation of the target object is not solved<br />
Field Data Description<br />Field Data (raw test data)<br />Collected from a rectangular grid of size 60m x 16m<br />EMIR is collected for 7 frequencies (widely separated) <br />330 990 3030 6030 13050 21300 43080<br />Classes 1- DU at surface, 2- DU at 30cm, 3- DU at 60cm, 4- clutter<br />
Feature Selection <br />ω3<br />ω4<br />Relevant features: Four spectral values of quadratic component centered around the peak value in the region of interest are<br />ω5<br />ω6<br />ω2<br />ω7<br />ω1<br />Motivation<br />Quadratic component of EMI response of 1 inch DU rods has a peak at around 3.5kHz <br /> The relation between the peak value and the values from its neighboring bands can <br />be very useful for characterizing DU metal of the same radii.<br />
Multi-stage Classification<br />Feature Vectors<br />Central features are the spectral values at <br />Central Features > thr1<br />YES<br />NO<br />Threshold thr1 is determined from the histogram of the corresponding feature<br />Central Features > other Features<br />Background<br />YES<br />NO<br />Other Metals<br />Feature Subset<br />One-Class SVM Training<br />PDF Estimation<br />Map Generation<br />Clustering<br />Class Map<br />PDF Visualization<br />Class Separation<br />
PDF Estimation <br /><ul><li>The PDF has two distinct Gaussian peaks
The Gaussian peaks correspond to the centers of two Gaussian distributions,thus two clusters</li></li></ul><li>Clustering<br /><ul><li>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</li></li></ul><li>Classification Map<br />
Validation<br />EMI Response is measured for seven metal cylindrical rods of 4inch length and 1inch diameter at 29 frequencies from 90 Hz to 90KHz. <br />Target response is basically quadratic response of objects at selected locations from classification map<br />Laboratory Measurements with GEM-3 sensor<br />Linear Model<br />Target Response<br />Reference Signatures<br />Best fit<br />
Validation Contd.<br />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. <br />Depth vs. Magnitude of EMI Response for compact objects<br />The magnitude of H field inversely depends on Nth power of the distance <br />Thus objects of class 1 have higher magnitude as they are closer to surface<br />Class 2 objects are much deeper thus relatively weaker signal strength<br />Corresponds to Quadrature response of DU objects at the surface (Class 1)<br />
Mean EMI Response<br />Corresponds to DU objects at 30cm depth<br />Supported by the reduced magnitude of the EMI response<br />Non-DU metal object with different EMI signature (clutter)<br />
Performance<br />DU Objects<br />Multistage approach with SVM for DU discrimination and depth separation<br />Confusion Matrix<br />Clutter<br />Soil Background<br />Average accuracy<br /> 95 %<br />
Summary<br />Unsupervised classification of different objects at different depths using a multistage learning method with OCSVMs is shown to be quite successful<br />This method is validated by comparing the target signatures from each class with laboratory measurements<br />Extension of this work is testing the detection and/or discrimination algorithm in the presence of substantial clutter and variable size DU objects<br />
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