Le radar de pénétration du sol (GPR) est une technique de télédétection employée pour obtenir l'endroit et la réflectivité spatiaux des objets enterrés. Puisque la plupart des antennes de GPR ne sont pas directives, les signaux dispersés enregistrés par le radar se prolongent au-dessus d'une grande ouverture latérale [1, 2]. Dans cette étude, des algorithmes de débruitage et de migration sont employés pour refocaliser les signaux dispersés de nouveau à leur point d'origine. Les données ont été prises pour différents scénarios. Afin de réaliser la séparation optimale de la signature de la cible de la réponse du soil, techniques de débruitage son utilisées sur les données 2D. La transformée de Hough randomisé est employé pour extraire des informations importantes [3]. Ces informations sont incluses dans un algorithme de migration [4], et la largeur aproximée de l'objet dans la direction du balayage est trouvée. Bien que les résultas sont pormetteurs, les algorithmes doivent toujours être validés dans différentes conditions

LOPERA, Olga

MACQ, Benoit

MILISAVLJEVIC, Nada

SCHEERS, Bart

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Denoising and Migration Techniques for Target Identification
from Ground Penetrating Radar 2D data; a case study
Olga LOPERA1,2, Nada MILISAVLJEVIC1, Bart SCHEERS1, Benoıˆt MACQ3
1Signal and Image Centre, Royal Military Academy
30 Av de la renaissance, 1000 Brussels, Belgium
2Dept. of Electrical and Electronic Engineering, University of Los Andes
Kr 1 Este 18A-10 Bogota, Colombia
3Telecommunications Laboratory, Catholic University of Louvain
2 Place du Levant, 1348 Louvain-la-Neuve, Belgium
olopera@elec.rma.ac.be
Re´sume´ – Le radar de pe´ne´tration du sol (GPR) est une technique de te´le´de´tection employe´e pour obtenir l’endroit et la re´flectivite´ spatiaux
des objets enterre´s. Puisque la plupart des antennes de GPR ne sont pas directives, les signaux disperse´s enregistre´s par le radar se prolongent
au-dessus d’une grande ouverture late´rale [1, 2]. Dans cette e´tude, des algorithmes de de´bruitage et de migration sont employe´s pour refocaliser
les signaux disperse´s de nouveau a` leur point d’origine. Les donne´es ont e´te´ prises pour diffe´rents sce´narios. Afin de re´aliser la se´paration
optimale de la signature de la cible de la re´ponse du soil, techniques de de´bruitage son utilise´es sur les donne´es 2D. La transforme´e de Hough
randomise´ est employe´ pour extraire des informations importantes [3]. Ces informations sont incluses dans un algorithme de migration [4], et la
largeur aproxime´e de l’objet dans la direction du balayage est trouve´e. Bien que les re´sultas sont pormetteurs, les algorithmes doivent toujours
eˆtre valide´s dans diffe´rentes conditions.
Abstract – Ground-penetrating radar (GPR) is a remote sensing technique used to obtain the spatial location and reflectivity of subsurface
objects. Since most GPR antennas have board beam widths, the scattered signals recorded by the radar extend over a large lateral aperture [1, 2].
In this study, denoising and migration algorithms are used to refocus the scattered signals back to their point of origin. The data were collected
for different scenarios. In order to achieve optimal separation of the target signatures from the background, denoising techniques are used to filter
the signal from the 2D data. The randomized Hough Transform is used to extract relevant features [3]. This extracted information is included in
migration algorithms [4], and the approximated width of the target in the scan direction is found. Although the results are promising, algorithms
still need to be validated under different conditions.
1 Introduction
Since the WWII, many military conflicts around the world
have left more than 100millions of antipersonnel mines (APM)
landed across the globe [5]. In Colombia, four decades of ci-
vil war have left more than 100 thousand antipersonnel (AP)
mines landed across 422 municipalities (40% of the national
territory). These mines cause approximately 150 deaths per
year, which a 31% are children [6]. These mines are American
and Belgian, as well as mines that were made for the National
Military Industry INDUMIL. However, a considerable amount
was made in an artisan way by the guerrillas (e.g., FARC and
ELN 1) . These last ones are called improvised explosive de-
vises (IEDs), and are the principal problem in Colombian de-
mining activities, due to their non-metallic content. In recent
years, serious efforts for developing technologies that can help
in landmine detection have been done [7]. Among these tech-
nologies, a great importance is given to the GPR because of
the ability to detect metallic and low-metallic AP landmines
by non-invasive subsurface sensing. Still, its main drawback is
the complex nature of its data, and then their interpretation is
usually limited in defining general ”areas of interest” instead of
1. Fuerzas Armadas Revolucionareias de Colombia - FARC, Ejercito de Li-
beracion Nacional - ELN
accurately determining the shape and position of the target.
In this study, two-dimensional data-sets (B-scans) from a
commercial time-domain GPR (800 MHz) are analyzed in or-
der to extract relevant information. These data-sets are collec-
ted in different scenarios. In Section II, a description of the tar-
gets and the measurements setup are given. In order to achieve
optimal separation of the target signatures from the background,
the problem is viewed as an image-processing one as the collec-
ted two-dimensional data-sets represent image segments. Sec-
tion III is devoted to the denoising techniques used to remove
unwanted reflections and to extract relevant pieces of informa-
tion as target position and depth, and the dielectric permittivity
(real part) of the soil. Migration theory is then applied for focu-
sing the scattered signals back in their point of origin. The Stolt
migration is used and is introduced in Section IV. The analysis
of the migrated data is presented in Section V. Finally, Section
VI concludes our study.
2 Measurement Setup
The data are acquired at the test land of the Military School
of Engineering, Colombia, for sandy and loamy soils (see Fig.
1). In this study we focus on the signatures of two types of
FIG. 1: Test land at the Military School of Engineering, Co-
lombia
AP mines: a Colombian AP mine and an improvised explosive
devise made by the guerrillas. In the remainder of this paper
these mine types will be referred to as APM A and B respecti-
vely. AP mine A has a plastic casing and low metallic content.
AP mine B has a plastic casing and non-metallic content. Their
dimensions, burial depths (from the top of the mine), and dis-
tance (from a reference point to the middle of the mine along
the scanning direction) are summarized in Table 1.
For the acquisition we used a monostatic radar system in the
time-domain developed by the Mala GeoScience Group 2, with
a 800MHz antenna and a repetition frequency of 100kHz. All
data have been recorded using a 10 ns time window of 512
samples corresponding to a time sampling interval of approxi-
mately 20ps. The resolution of each sample point is 16 bit. The
data have been acquired along one surface direction and the
antenna system was positioned above the soil surface. The dis-
tance was measured by an incremental encoder connected to
the system and the antenna was moved whit a constant velocity.
In Fig. 2, the collected data for the loamy soil is represented in
function of the scanning distance and the time-of-flight of the
signal (time to reach the reflection point and to come back to
the receiver antenna) for the two types of mines.
TAB. 1: Target features*
Target Dimension (m) Depth (m) Distance (m)
APM A d:0.075, h:0.07 0.01 0.60 (l), 0.60 (s)
APM B d:0.120, h:0.07 0.10 0.60 (l), 0.60 (s)
* d: diameter, h: height, (l): loamy soil, (s): sandy soil
3 Ground Clutter Subtraction
To eliminate the clutter from an image we have first to de-
fine it. The electromagnetic waves transmitted by the radar are
propagated through the air and, of course, to the ground. At
any interface between any two media having different elec-
tromagnetic properties, part of the electromagnetic energy is
reflected backwards to the receiver and a part is transmitted
into the second media. The same phenomenon occurs when
the transmitted signal comes across a buried object (of which
2. http://www.malags.se
electromagnetic parameters differ from those of the ground)
and, unfortunately, across any other non-uniformities inside the
ground (stones, layers having different electromagnetic para-
meters, etc). All these reflections which are not related to the
object’s own scattered field are called clutter [8].
Two denoising methods are used to filter the ground clutter
level. These methods are implemented by convolving the image
with a horizontal high pass filter. The derivative of a Gaussian
represents such a filter. It is important to notice that the win-
dow width of the filter is crucial for the final result. The choice
of the filter length depends on the size and resolution of the
image. Several tests show that a too small window introduces
noise, and a large window minimizes the contrast of the image
[3]. We achieved the best results with a 10-pixels wide filter.
Every change in the image resolution or image size needs a
reevaluation of the ideal filter width. Note that a vertical noise
was introduced to the image (because horizontal filtering is a
derivative action). To reduce this effect, a vertical 1x5-pixels
filter was created by a Gaussian function. This filter is used to
average over 5 pixels in order to reduce the noise in the vertical
direction. Once the image is filtered, we can see the hyperbolic
response of the target. An algorithm to detect curves is applied
for extracting relevant information from the equation of the hy-
perbola. The Randomized Hough Transform is used [3]. RHT
randomly selects n pixels from an image and fits them to a pa-
rameterized curve. If the pixels fit within a tolerance, they are
added to an accumulator with a score. Once a specified num-
ber of pixel sets are selected, the curve with the best score is
selected from the accumulator and its parameters are used to
represent a curve in the image. In RHT, if a curve in the ac-
cumulator is similar to the curves being tested, the parameters
of the curves are averaged together and the new average curve
replaces the curve in the accumulator. The extracted informa-
tion form the hyperbola equation in included in the migration
algorithm.
4 Phase-shift Migration
Phase-shift migration was first applied for seismic imaging
by Stolt in 1978 [9]. The principle has been adopted for sa-
tellite SAR processing [10] and it has also been widely utilized
for processing of GPR data in two dimensions [11]. Phase-shift
migration is based on a transformation from the frequency do-
main ω to the wavenumber domain k. Consider the raw data set
b(x, z, t) collected from the radar, x being the B-scan distance,
z the depth, and t the time-of-flight. Applying a 2D Fourier
transform with respect to the spatial distance x and the time t
to spatial frequency kx, the result is an unfocused wavenumber
data set
B(kx, z, ω) =
∫ ∫
b(x, z, t)eikxx−iωtdxdt. (1)
The Fourier transformation along the x coordinate only makes
sense if the propagation velocity does not vary in this direction.
The method allows variations of the propagation velocity in the
z direction. Defining the wavenumber vector k as the vector
sum of kx and kz for one-way propagation, we have:
k = |k| =
√
k2
x
+ k2
z
=
ω
υ
=
2pi
λ
, (2)
 x (m)
 
t (
s)
0.2 0.7 1.2
1
2
3
4
5
6 −3
−2
−1
0
1
2
3
x 104
0
x10−9
(a)
 x (m)
 
t (
s)
0.2 0.4 0.6 0.8 1 1.2
1
2
3
4
5
6
7
8
−3
−2
−1
0
1
2
3
x 104
0
x10−9
(b)
FIG. 2: B-scans on loamy soil for APM A (a) and B (b).
where υ is the propagation velocity of the ground (υ =
c/
√
εr, being εr the relative dielectric permittivity, extracted
from the equation of the hyperbola) and λ is the wavelength
in the ground. The direction of the k-vector is identical to the
traveling direction of a plane wave propagating from the tar-
get to the antenna. Assuming only upward coming waves, and
by introducing kz from (2) in (1), the Fourier transform of the
wavefront at depth z is done by
B(kx, z, ω) = B(kx, 0, ω)e
−ikzz. (3)
The migrated data will be the inverse Fourier transform of
(3) at time t=0:
b̂(x, z) = b(x, z, 0) =
∫ ∫
B(kx, 0, ω)e
−i(kxx+kzz)dkxdω.
(4)
Equation (4) is the general representation of the phase-shift
migration. The implementation of this method is computatio-
nally intensive, because of the number of floating point opera-
tions needed for migration [2]. For the reduction of the calcu-
lation time, a variant of the phase-shift migration for a constant
propagation velocity is used. This variant was developed in [9].
In the special case where υ(z) is constant, (4) can be further de-
veloped by changing the variable dω to dkz . By replacing dω
from (2), the data must be scaled by the Jacobian of the trans-
formation from ω to kz , kzυ
2
ω
. Hence, for the Stolt migration,
(4) becomes
b̂(x, z) = υ2
∫ ∫
kz
ω
B(kx, 0, ω)e
−i(kxx+kzz)dkxdkz. (5)
5 Results and Discussions
In Fig 2, the B-scans on loamy soil for AP mine A and B
are shown. The reflection of the buried object is completely
drowned in the ground reflection (between 0.5 ns and 1.5 ns).
By applying denoising and segmentation techniques, relevant
information can be extracted for the data. It is summarized in
Table 2. It can be seen that the extracted values are quite close
to real ones. The differences between the real values and the
calculated ones are due to the three-dimensional form of the
APM. The depth and distance were measured respectively from
the top and from the middle of the mine. However, the backs-
cattered signal received by the antenna comes from different
points over the surface of the mine [2], and then different hy-
perbolas will be appear in the B-scan. By averaging these dif-
ferent hyperbolas, we will find a very good approximation of
the position of the mine, but not the exact one.
TAB. 2: Calculated depth and distance*
Target Depth (m) Distance (m)
APM A 0.0078 0.671 (l), 0.624 (s)
APM B 0.1056 0.652 (l), 0.636 (s)
* (l): loamy soil, (s): sandy soil
In Fig 3, results of applying the migration algorithm for the
denoised data are shown. From this figure, the shape of the tar-
get on the B-scan direction can be seen. For the APM A, it ap-
pears at 0.5 ns. This points out that the AP mine is placed at the
soil surface. For the AP mine B, it appears at 1.0 ns. This points
out that the mine is buried close to the surface (∼ 10 cm). Over
the scanning axis, the position of the target (the shape of the
target) appears quite close to the calculated and real position of
the target in the soil.
6 Conclusions
In this paper, denoising and migration methods have been
applied on real GPR data. The data are collected in two types of
soil and for conventional and non-conventional APM. Namely,
the major limit factor to detect APM with GPR is the ground
clutter. To reduce the ground clutter an horizontal filtering is
applied. Since the buried object signature on GPR 2D data are
represented by a hyperbolic shape, the RHT is applied for the
isolation of the object response. The relevant pieces of informa-
tion that have been extracted from the equation of the hyperbola
give us a very good approximation of the position of the buried
object. The size of the object can be deduced from the migrated
data. All this information can be used in data fusion architec-
tures, involving other sensor data. The performance of these
algorithms on real GPR data is encouraging. These algorithms
are not computationally intensive. However, it depends on the
 x (m)
 
t (
s)
0.2 0.7 1.2
1
2
3
4
5
6
−1
−0.5
0
0.5
1
1.5
x 104
0
x10−9
(a)
 x (m)
 
t (
s)
0.2 0.4 0.6 0.8 1 1.2
1
2
3
4
5
6
7
8
−4
−2
0
2
4
6
x 104
0
x10−9
(b)
FIG. 3: Results of applying denoising and migration techniques for B-scans on loamy soil for APM A (a) and B (b).
size of the database. In order to make an objective analysis, the
overall processing still needs to be validated in different condi-
tions and for more than one object buried in the soil.
Acknowledgment
This research project is carried out at the Signal and Image
Centre of the Royal Military Academy, Belgium, and at the De-
partment of Electrical and Electronic Engineering of the Uni-
versity of Los Andes, Colombia, in collaboration with the Te-
lecommunications Laboratory, Catholic University of Louvain,
Belgium. It is supported by a research grant funded by the Mi-
nistry of Defence of Belgium, in the scope of the BEMAT pro-
ject, and by a research grant from Colciencias (Colombian Ins-
titute for the Development of Science and Technology, Colom-
bia). The authors would like to thank the Colombian Military
Forces for their support, specially the personnel of the MARTE
group and the BAMAI laboratory.
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Denoising and migration techniques for target identification from ground penetrating radar 2d data; a case study

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