Spectral Analysis for the Detection of Explosives with
Differential Reflectometry
Seniha Esen Yuksel
Thierry Dubroca
Rolf E. Hummel
Dept. of Computer,
Information Science and
Engineering
University of Florida
Gainesville, FL
Dept. of Material Science and
Engineering
University of Florida
Gainesville, FL
Dept. of Material Science and
Engineering
University of Florida
Gainesville, FL
[email protected]
[email protected]
[email protected]
Paul D. Gader
Dept. of Computer,
Information Science and
Engineering
University of Florida
Gainesville, FL
[email protected]
ABSTRACT
For explosive detection purposes, it is assumed that the person preparing or carrying the explosive will inadvertently
contaminate him/herself or the exterior of the package. To
detect such traces of explosive materials, we show the use
of differential reflectometry (DR) as an alternative system
to the existing techniques. With DR, explosives show characteristic behaviours at specific wavelengths, for example,
spectra of TNT shows a sudden decrease at 420 nm. To
detect these behaviours, principle component analysis was
performed to reduce the dimensionality of the data, and
a support vector machine classifier was trained to identify
TNT. With a 10-fold classification on 10000 non-TNT and
1935 TNT pixels, we achieved 0.3% false alarm rate at 75%
true positive rate. In this study, we outline the operation
of the DR system, show the unique signatures of explosives
when viewed with DR, and report the detection rates with
support vector machine classifiers.
Keywords
Explosive detection, hyper-spectral imaging, spectroscopy,
differential reflectometry, classification, SVM, TNT.
1. INTRODUCTION
One of the current explosive detection methods that is being
used in the U.S. airports is ion mobility spectroscopy (IMS).
In this system, a technician wipes a cloth over a piece of luggage which in turn is placed into the IMS device that heats
this swab to vaporize the particles to be investigated. The
particles are ionized by electron bombardment and then accelerated in an electric field. Different ions have different
speeds. Using the speed information the presence of nitrogen can be deduced [9]. However, IMS has a number of
disadvantages; for example, the time required for wiping an
entire bag does not allow to investigate all pieces of luggage
inside and outside which means that the technician has to
make some judgment which bag to investigate. On the other
hand, if all bags are screened, the slow process leads to long
lines at the airports. Also, IMS cannot differentiate between
explosives and other nitrogen containing substances such as
fertilizer, cosmetics and certain polymers which often lead
to false positives [5]. Most of all however, IMS needs the
involvement of an operator which is costly.
For detection of explosives on humans, the so-called “puffer”
has been tried which involves blowing air in a closed cell
to dislodge possible explosives, then vacuum the air in this
cell to direct it into an IMS device, as just described [5, 6].
However, it has been shown that explosive molecules can
stick to surfaces with relatively high binding energies [4, 6]
which may preclude the release of the explosive vapor.
In order to alleviate these problems differential reflectometry (DR) has been developed [3] at the University of Florida.
This technique, also called as differential reflection spectroscopy, measures the differential reflection from materials
at multiple wavelengths. With DR, explosives show signature patterns at specific wavelengths, and with the design
of classifiers that can identify these signatures, it should be
possible to eventually check every piece of luggage, cargo,
and passengers that boards an aircraft. This device does
not require the physical transfer of the explosive substance
2. DIFFERENTIAL REFLECTOMETRY
Our group at the University of Florida has developed a device that detects explosive materials on surfaces such as luggage, parcels, or mail. In this device, a broad band ultra
violet (UV) -visible light source is shone onto a conveyor
belt. This light source produces a 5mm by 50cm line on
the conveyor belt as shown in Fig. 1. Any material, such as
a parcel moving on the conveyor belt, is probed with this
light source. Then, the photons reflected off the surface are
collected and sent through a spectrometer that separates
the broad band light into the wavelengths between 100nm
and 612nm. These measurements are recorded with a CCD
camera that has 512 × 512 resolution. In this system, the
spectra collected from each pixel of the probed surface is the
reflectivity at that pixel, denoted by R. Two reflectivities
are measured from two adjacent parts of the same surface,
denoted be R1 and R2 , and differential reflectogram (DR) is
computed as the normalized difference in reflectivity, given
by
ture in that it shows a significant drop at 420nm. This behaviour is shown in Fig. 2 where visually similar substances
such as TNT, salt, Splenda, and flour are placed on a carbon
pad at 12cm from the sensor, and their spectra in the 100nm
to 612nm range are plotted. Note that TNT is one of the
most common explosives [7], and other common explosives
such as RDX, C4 and PETN show characteristic shoulders
at other wavelengths. One could therefore identify which
material (explosive) is present on the probed surface [3, 8].
At 12 cm from sensor
1
0.5
DeltaR / Rbar(a.u.)
into a detection device, it is portable, fast ( 1 sec) and safe,
and it can be combined with X-ray scanner which predominantly detects metals.
0
−0.5
−1
CarbonPad
TNT
Salt
Splenda
Flour
−1.5
132 164 196 228 260 292 324 356 388 420 452 484 516 548 580 612
Wavelength(nm)
∆R/R̄ = 2(R2 − R1 )/(R1 + R2 ).
This normalization reduces the fluctuations of the line voltage, the intensity variation of the light source, and other low
frequency variations.
Figure 2: Spectral signatures of other white substances (salt, Splenda, and flour) that are visually
similar to TNT. All these substances were placed
on a carbon pad, the signature of which is shown in
blue (zero line). Each color represents a different
material, and each spectra corresponds to one pixel
of the probed material.
2.1
Challenges in explosive detection with DR
Challenges in explosive detection with DR can be listed as:
• Detecting trace amounts of explosives: As the amount
of the explosive decreases, the signal to noise ratio
(SNR) decreases and it becomes harder to detect the
trace amounts of explosive materials.
• Detecting from a far: Detection gets harder as the
distance from the sensor to the probed material gets
wider.
Figure 1: The plot of the current explosive detection
system. The distance from the light source to the
conveyor belt is 47 cm. By design, the collection
angle of the detector is 103o , which corresponds to
50cm width at 40cm height.
Absorption band in the DR spectra represents energies where
photons are absorbed by electrons within the material. Only
photons with large enough energies which raise electrons to
higher energy levels are absorbed. TNT has a unique signa-
• Noise in the system: Noise reduction techniques need
to be addressed to remove the noise due to the light
source, the camera and the line voltage.
• Explosives that are not pure: The spectra may deviate
from its characteristic behaviour if the explosive material is diluted, and if it is mixed with other materials
such as flour, make-up, hand cream etc. To solve such
cases, sub-pixel techniques need to be used. Sub-pixel
means the amount of TNT present in the pixel does
not cover the entire pixel, for example, the pixel could
be 10% TNT and 90% cloth.
• Signature of the underlying material: Although the
signatures of various explosives have been compared
to several other materials (both synthetic and natural), and have been shown to be unique identifiers in
[2, 3], we are continuously investigating the signatures
of other materials that unusually textured and multipatterned.
the amount deposited on the surface. At the thin edge of
the triangular shaped sample, the amount is very small and
therefore, the signal to noise ratio is very low. For such small
quantities, a subpixel analysis such as end-member detection
[11] is required.
In this rest of this study, we will explain some of our experiments and our solutions to tackle these challenges.
The camera’s CCD is composed of 16 channels or taps, as
shown in Fig. 3. At the interface of two channels, the pixels
are interlaced, and therefore, a sharp noise arises at these
pixels when we compute the normalized difference. To remove this noise, a median filter is applied. Furthermore, in
order to remove the high frequency white noise, we apply a
moving average filter. Frequency in this setting is meant to
be the reciprocal of nm’s (noise within a spectrum).
Figure 4: TNT is smeared as a triangle on a carbon
pad, which is then placed on a light absorbing pad.
The carbon pad is a good absorber of light, and can
be used as a marker as it gives a zero mean signal
in the differential reflectometry.
TNT on a triangle mask
0.5
DeltaR/Rbar(a.u.)
2.2 Noise removal
0
−0.5
−1
−1.5
260
292
324
356
388
420
452
484
516
548
580
Wavelength(nm)
Figure 3: Channels in the CCD camera. At each
intersection of the channel, a sharp noise is observed,
which is then filtered with a median filter.
Figure 5: TNT spectra for a sample of the pixels on
the triangle mask. Each green line is a TNT spectra
from one pixel. One of the pixels that is at the thin
edge of the triangle has been marked in red, and
another pixel that has a good amount of TNT is
marked in blue.
2.3 Amount of the explosive material
2.4
To understand the effect of decreasing the explosive amount,
TNT was placed on a carbon pad in a triangular fashion as
shown in Fig. 4, and the pixel numbers corresponding to the
start and end of the TNT were noted. The carbon pad’s
DR is a flat line with added noise, and the spectra of TNT
particles show a deviation from the flat line at the 420nm.
A sample of the TNT spectrum from this area is given in
Fig. 5 where one green line represent the spectra of one pixel.
One of the pixels that is at the thick edge of the triangle is
marked in blue, and one pixel that is at the thin edge of
the triangle is marked with red. In Fig. 6, the height of
the drop (shoulder) has been plotted as a function of the
pixel number. It can be observed that the signal strength
(height of the 420nm shoulder for TNT) is proportional to
In our experiments, we placed TNT in powder form within
a triangular shape on pieces of various clothes with different
textures and colors. The conveyor belt is at 47cm from the
sensor, and the cloth was placed at 27cm from the sensor.
Hence, the area of each pixel is 0.6 × 5mm2 . The long edge
of the TNT sample is 19.1mm long. In order to correctly assign the collected spectra with its respective material, pink
PTFE tape was placed as a marker at each end of the triangular shaped sample, as shown in Fig. 7. The pink tape
has an absorption spectrum that is easily identifiable and
it is used to correctly label the smallest TNT pixel. The
spectrum of these materials are shown in Fig. 8 as an image
of wavelength vs. the pixel number. The signature of the
pink markers have been indicated with the black rectangles,
Analysis on clothes
Color indicates DeltaR/Rbar(a.u.)
0.2
290
0
280
−0.2
Pixel index
270
260
−0.4
250
−0.6
240
−0.8
230
−1
220
−1.2
210
−1.4
200
132 164 196 228 260 292 324 356 388 420 452 484 516 548 580
Wavelength(nm)
Figure 6: The height of the shoulder at 420nm plotted as a function of pixel number. The height is
around 0 when there is no TNT (pixels 0 − 10), and
almost linearly increases as we move to the thicker
edge of the triangle. When the area the light source
shone on is all covered with TNT, the height reaches
a saturation (pixels 40 − 60).
which show lower magnitudes, and no significant drop at
420nm. The TNT signature is in between the black rectangles, and shows a drop starting at wavelength 400nm.
Figure 7: TNT is placed on a piece of cloth in a
triangular shape of 19.1mm × 8.1mm. Pink markers
are placed at each side to mark the end and start
pixels of the explosive material.
Figure 8: The signature of the materials (red cloth
with TNT in Fig. 7 as an image of wavelength vs. the
pixel number. The pink markers have been marked
with the black rectangle. At pixel number 250, one
can observe the color change starting at wavelength
400nm, from red to blue to green and to orange. Observing the color bar on the right, this color change
indicates the signal drop at 420nm, the characteristic
signature of TNT.
we mean a couple of needle tips), and measurements from
10 clothes with trace amounts of TNT. This dataset results
in 1935 pixels with TNT, and 30800 pixels with non-TNT.
3.1
Dimensionality Reduction
The high dimensionality of the dataset (512 dimensions corresponding to the 512 different wavelengths) would be a
problem for most parametric classification models, therefore, we only considered the wavelength range 300 − 550nm
instead of the full 100 − 612nm.
Further, we applied Principal Component Analysis (PCA) to
reduce the dimensionality of all the signals. Consider a data
set of observations {xn } where n = 1, ..., N indicates the
data index, and xn is of dimensionality D. The goal of PCA
is to project the data onto a space with lower dimension M <
D while maximizing the variance of the projected data [1].
The procedure starts by finding the mean x of the training
set from
x=
3. EXPERIMENTAL RESULTS
The classification results are on a dataset that is a combination of many data collections from bags, clothes and carbon
pads with varying amounts of TNT. It includes 10 measurements from TNT, salt, Splenda and flour measured on a
carbon pad at 40, 29, 20 and 12cm from the sensor; 5 experiments from TNT on a triangle mask measured from a carbon
pad; 5 measurements from one bag with decreasing amounts
of TNT, and 4 other bags placed at 19cm from the sensor
with trace amounts of TNT (with trace amounts of TNT,
N
1 X
xn
N n=1
,
(1)
and the covariance matrix S of the training set from
S=
N
1 X
(xn − x)(xn − x)T
N n=1
.
(2)
From the covariance matrix, the eigenvectors and the eigen-
Plot of the log of the eigenvalues
Eigenvalues
10
Significant eigenvectors
0.2
0.15
Magnitude of the Eigenvector
values are computed, and the M eigenvectors that correspond to the largest M eigenvalues are stored. The eigenvalues of the covariance matrix for our data is shown in Fig. 9
in the log scale. We considered the first 6 of these eigenvalues to be important, hence M = 6, and the corresponding
eigenvectors ui , i = 1, ..., 6 are shown in Fig. 10. The data
was then transformed onto this space with xnew
= ui x for
i
each of the eigenvectors.
0.1
0.05
0
−0.05
−0.1
eigenvector 1
eigenvector 2
eigenvector 3
eigenvector 4
eigenvector 5
eigenvector 6
−0.15
5
−0.2
0
−0.25
300
350
400
450
500
550
Wavelengths
−5
Figure 10: The six significant eigenvectors. The second eigenvector, marked in blue, is a good indicator
of TNT signature.
−10
−15
−20
−25
0
50
100
150
200
250
300
Number of eigenvalues in order of decreasing magnitude
Figure 9: The log of the eigenvalues obtained from
PCA. The first six values were considered important
for dimensionality reduction.
3.2 Classification
On the transformed space, we applied Support Vector Machines (SVM) for classification. SVM is a well-studied classifier (see [10]), and aims to find a hyper-plane that maximizes
the margin among the two classes [1]. For a feature-space
transformation φ(xn ) that is related to a Mercer Kernel,
SVM tries to find a hyper-plane wT φ(x)+b = 0 in the kernel
space, that has the largest distance to the nearest training
data points of any class. To find the parameters w and b
of the hyperplane, SVM solves a constrained optimization
problem given as:
N
X
1
αn [yn (wT φ(xn ) + b) − 1]}
min max{ kwk2 −
α
w,b
2
n=1
where α = (α1 , ..., αN )T are Lagrange multipliers, and yn
is either 1 or 1 such that {yn ∈ {−1, 1}}N
n=1 , indicating the
class to which the point xn belongs.
In order to classify new data points using the trained model,
we evaluate the sign of y(x), defined by:
y(x) = wT φ(x) + b .
In order not to bias the SVM model towards non-TNT data,
10000 spectra were randomly selected among the 30800 nonTNT spectra. These randomly selected 30800 non-TNT pixels as well as the 1935 TNT pixels were provided to SVM
with a 10-fold cross-validation. In 10-fold cross validation,
the dataset is partitioned into 10 subsets. Of the 10 subsets, a single set is retained for testing, and the remaining 9
subsets are used for training; and this operation is repeated
10 times, with each of the 10 sets used exactly once as the
testing data. The receiver operating characteristics (ROC)
for a 10-fold training with a linear SVM model (φ(x) = x )
is given in Fig. 11.
We achieve a 0.3% false alarm rate for 75% true positive
rate as displayed in Fig. 11. Although the ROC curve may
seem good, it corresponds to roughly 1536 false alarms in a
20 × 5cm2 area. Therefore, our ongoing efforts are towards
combining the SVM classifier with sub-pixel detection methods. It would also be interesting to see the relation of the
significant eigenvectors of PCA with the end-members of the
sub-pixel detection methods. Also, a spatial analysis of the
pixels would be very useful to decrease the amount of false
positives. If a pixel has been identified to be TNT with a
low probability, and does not have any neighbouring pixels
that are labelled as TNT, then this information could be
used to reduce the false alarm rate.
Receiver Operating Characteristic (ROC)
D. Hedden, T. Thundat, and R. T. Lareau.
Adsorption-desorption characteristics of explosive
vapors investigated with microcantilevers.
Ultramicroscopy, 97(1–4):433 – 439, 2003.
1
0.9
True Positive Rate
0.8
[5] J. E. Parmeter, D. G. A. Eiceman, and J. E.
Rodriguez. Trace detection of narcotics using a
preconcentrator/ion mobility spectrometer system.
Technical Report 602 – 00, National Institute of
Justice, 2001.
0.7
0.6
0.5
0.4
0.3
[6] P. Rodacy. The minimum detection limits of RDX and
TNT deposited on various surfaces as determined by
ion mobility spectroscopy. Technical report, Sandia
National Labs., Albuquerque, NM, Aug 1993.
0.2
0.1
0
0
0.2
0.4
0.6
0.8
1
False Positive Rate
Figure 11: Receiver operating characteristics from
a 10-fold training with SVM.
4. CONCLUSION
In this study, we investigated a DR system to detect TNT
fast and remotely. The DR system measures how the explosives modify the reflectivity properties of the surface they
are placed on. TNT shows a signature behaviour at 420nm
when measured with this system. To detect TNT, we used
PCA to decrease the dimensionality of the data from 512 to
6. On this transformed data with 6 dimensions, we employed
an SVM classifier to identify TNT on bags and clothes. Our
results of 0.3% false alarm rate at 75% true positive rate
indicate the usefulness of the approach.
Our future work will include sub-pixel detection methods
to decrease the false alarm rates, as well as analyzing other
explosive materials such as C4 and PETN on clothes with
different textures. Nonetheless, our results show that different reflectometry could be a fast alternative to the other
methods that are currently in place, and can provide very
useful information to reduce the false positives.
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