260 ieee transactions on geoscience and remote …

260 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 2, FEBRUARY 2006

An Abrupt Change Detection Algorithm forBuried Landmines Localization

Delphine Potin, Philippe Vanheeghe, Senior Member, IEEE, Emmanuel Duflos, Member, IEEE, and Manuel Davy

Abstract—Ground-penetrating radars (GPRs) are verypromising sensors for landmine detection as they are capableof detecting landmines with low metal contents. GPRs deliverso-called Bscan data which are, roughly, vertical slice imagesof the ground. However, due to the high dielectric permittivitycontrast at the air–ground interface, a strong response is recordedat early time by GPRs. This response is the main component ofthe so-called clutter noise and it blurs the responses of landminesburied at shallow depths. The landmine detection task is thereforequite difficult. This paper proposes a new method for automateddetection and localization of buried objects from Bscan records.A support vector machine algorithm for online abrupt changedetection is implemented and proves to be efficient in detectingburied landmines from Bscan data. The proposed procedureperformance is evaluated using simulated and real data.

Index Terms—Clutter reduction, ground-penetrating radar(GPR), landmine detection, novelty detection.

I. INTRODUCTION

THE global landmine crisis is one of the most pervasiveproblems facing today’s world. It is estimated that 45 to 50

million landmines are buried in the ground of at least 70 coun-tries. Landmines reportedly maim or kill 20 000 civilians everyyear. Beyond the immediate dangers to life and limb, landminesimpose a heavy economic burden on the mine-affected com-munities (www.landmines.org). Eliminating landmines is thusa major issue, and it entirely depends on the ability of systemsto detect buried landmines.

The detection problem is made complicated by many land-mines types and burying areas: roads, fields, buildings, forests,and deserts. State-of-the-art systems include various sensorssuch as ground-penetrating radars (GPRs), infrared cameras,metal detectors, etc. In this paper, we focus on GPRs: theyare very promising as they can detect plastic as well as verylow metal contents landmines. There are two kinds of GPRsused in landmine detection applications: frequency-steppedcontinuous-wave (FSCW) radars and pulse radars [1]. FSCWradars emit stepped radio-frequency signals toward the groundand records the response. Pulse radars emit short durationelectromagnetic pulses which propagate into the soil and reflecton the dielectric permittivity discontinuities. When recorded at

Manuscript received March 16, 2005; revised August 2, 2005. This work wassupported by the Fondation Norbert Ségard.

D. Potin is with the Institut Supérieur d’Électronique et du Numérique,Équipe de Recherche en Automatique des Systèmes et Microsystèmes(ISEN-ERASM), 59046 Lille Cedex, France (e-mail: [email protected]).

P. Vanheeghe, E. Duflos, and M. Davy are with the Laboratoire d’Automa-tique, Génie Informatique and Signal (LAGIS), CNRS UMR 8146, EcoleCentrale de Lille, 59651 Villeneuve d’Ascq Cedex, France (e-mail: [email protected]; [email protected]).

Digital Object Identifier 10.1109/TGRS.2005.861413

Fig. 1. Typical Ascan record obtained by a GPR pulse radar. Clutter ischaracterized by large-amplitude oscillations at early times. This Ascan isrepresented by a vertical dashed line on the Bscan of Fig. 2.

a given location the recorded pulse radar response is an Ascan,which actually is the magnitude of the reflected wave withrespect to time. Due to propagation time, waves reflected on anobject arrive to the GPR with a time lag which is related1 to thedistance between the object and the GPR. The image obtainedby concatenating Ascans recorded along a survey line is calleda Bscan. The horizontal axis of a Bscan corresponds to theGPR spatial location2 whereas the vertical axis corresponds totime (i.e., depth). A Bscan can be seen as an image of a verticalslice of the ground. Typical Ascan and Bscan recorded in thecontext of landmine detection are plotted in Figs. 1 and 2. Thispaper investigates landmine detection from pulse radar Bscans,as a step toward a multisensors detection system.

Pulse radars, for humanitarian demining, have the ability toscan the ground from the surface to one meter depth, whichis the required depth range. However, the emitted electromag-netic pulse strongly reflects at the air–ground interface. Thisresults in a hindering high-amplitude response which appearsat the early time of the Ascan. This phenomenon is known asclutter, and makes difficult the detection of landmines from As-cans/Bscans. More specifically, many landmines are laid flushwith the ground or buried at shallow depths (1–5 cm): their re-sponses to the GPR emitted pulse overlap with clutter. Moreovertheir metallic contents can be very low, their responses are there-fore much weaker than those coming from the air–ground inter-

1The wave arrival time lag is almost proportional to the buried object distance.The proportionality coefficient depends on the physical parameters of the soil.

2It is assumed here that the GPR is moved along a straight line. The horizontalaxis actually gives the distance covered by the GPR from its initial position.

0196-2892/$20.00 © 2006 IEEE

POTIN et al.: ABRUPT CHANGE DETECTION ALGORITHM FOR BURIED LANDMINES LOCALIZATION 261

Fig. 2. Bscan image obtained by concatenating consecutive Ascans recordedalong a survey line. Two typical landmine signatures (hyperbola) can be seenbelow the horizontal clutter stripes (see arrows).

face resulting in a poor signal-to-noise ratio. As stated above,clutter is mainly caused by the air–ground interface response.To a lower extent, it is also created by antenna coupling prob-lems and multiple reflections on the air–ground interface.

Many detection algorithms based on different signal pro-cessing approaches have been developed to localize landmineson Bscan data. Some approaches [2], [3] search for hyperbolas,which characterize the landmines responses on Bscans, so asto determine the presence of a landmine. In [2], the approachis based on fuzzy logic and neural networks whereas in [3],it is based on an adaptive approach using a least mean squarealgorithm for anomaly detection and polynomial fitting. Otherdetection algorithms require a GPR signals modeling. In [4],hidden Markov models are used to model the signal returnsfrom landmines and backgrounds and estimate the likelihoodthat signal returns are due to landmines. A support vectormachine based on an inverse scattering procedure is used in[5] to estimate the position of a buried object. This approachrequires a mathematical model of the electric field scattered byburied targets. Statistical signal processing techniques have alsobeen used to localize buried landmines. In [6], the approach isbased on the use of linear prediction in the frequency domain:GPR vector samples are modeled by a linear prediction model.Then, a constant false alarm rate technique together withthe likelihood function of clutter samples are used to detectlandmines. In [7], a landmine detection algorithm based on theprincipal components analysis is used. The method consists indistinguishing Bscans that contain objects from Bscans that donot, based on their maximum likelihood distances calculatedin the subspace spanned by the principal components. In [8],a change detection method based on energy detection is usedto localize buried landmines on Bscan data. After clutter re-duction, changes are detected in the spatial and time directionin order to find the landmines horizontal positions and theirresponse times. Changes in both directions are detected thanksto a sequential probability ratio test.

In this paper, a new method based on abrupt changes detectionis proposed in order to detect and localize landmines in Bscans.Abrupt changes detection is a difficult signal processing task.Strong theoretical results hold in the case of known statistical

Fig. 3. Bscan of Fig. 2 before clutter reduction.

models of the data [9]. However, GPR data are difficult to modelaccurately since this consists in modeling the propagation ofelectromagnetic waves into an heterogenous soil. Model-basedstatistical techniques are therefore difficult to use in such a case.The core idea of our approach is to apply a nonparametric abruptchanges detection technique to Bscans, as follows.

• Step 1: abrupt changes are sought along the spatial (hori-zontal) Bscan axis.

• Step 2: abrupt changes are sought along the time (vertical)axis. Of course, Step 2 is only implemented in areas wherehorizontal abrupt changes have been detected. An impor-tant difficulty in Step 2 is that clutter might cause abruptchanges, it has thus to be removed.

This paper is organized as follows. Section II presents theclutter reduction technique to be used in Step 2. It consists ofapplying a digital high-pass filter to GPR data. This filter is de-signed from a geometrical model of both clutter and landminesignatures in a Bscan. The online abrupt change detection algo-rithm is detailed in Section III. It is based on data comparisonusing a support vector machine. Section IV describes the fulllandmine detection procedure. Simulations results on simulatedand real data are finally given in Section V.

II. CLUTTER REMOVAL METHOD

The chosen clutter reduction method is based on the use ofa two-dimensional (2-D) digital filter which is adapted to GPRdata. This method is fully described in [10] and can be summa-rized as follows. (In this paragraph, clutter is also referred to asnoise and the buried object response is referred to as signal.)

A. Clutter Characterization in the Frequency Domain

In a typical Bscan (see Fig. 3), the clutter appears as three hor-izontal bands. These bands have a very high contrast, i.e., theycan be accurately modeled as a rectangle function. Let Bscansbe defined as functions where represents the spatial co-ordinate ranging from 0 to and the time coordinate rangingfrom 0 to . Each clutter band on a Bscan can be modeled by afunction defined as follows:


Fig. 4. Zoom of the Bscan of Fig. 3. t and t indicate the edges of the firstclutter band.

with

where and are the time instants that delimit a clutter band;see Fig. 4. In order to design a clutter removal filter, we mapthe data into the frequency domain. Each clutter band has thefollowing 2-D magnitude spectrum:

(1)

where is the spatial frequency parameter, is the frequency,and is the width of a clutter band . By consid-ering that the main energy of such a function is located insidethe two first lobes of the sinc functions, the clutter energy is lo-cated inside the subspace defined by

such that

where .

B. Signal Characterization in the Frequency Domain

As opposed to clutter, a buried object appears as a hyperbolain Bscans. A landmine signature can therefore be modeled bya hyperbola. In the frequency domain, it is shown in [10] thatthe approximated magnitude spectrum of such a signal for fre-quency less than few gigahertz is given by

(2)

where is equal to half the hyperbola width recorded on theBscan.

C. Clutter Removal in the Frequency Domain

An important remark is that the magnitude of the signal spec-trum is almost independent of at the considered GPR signalfrequencies and its main energy is localized inside the interval

. In the spectral domain, the signal(buried object response) bandwidth is much larger than the noise

Fig. 5. Magnitude spectrum of the Bscan plotted in Fig. 2. A logarithmic scaleis applied on the magnitude spectrum.

Fig. 6. Bscan of Fig. 2 after clutter reduction. The landmine signatures are stillvisible.

(clutter) bandwidth: for example, in the Bscan in Fig. 3,m and cm, and the noise main energy is within

a spatial frequency band of width 2 m whereas the signal iswithin a band of width 20 m . Hence, the spread of the noisespectrum along the axis is very small in comparison with thespread of the signal spectrum. By using a 2-D digital high-passfilter with a very sharp transition band along the frequenciesaxis the clutter can be reduced without degrading the signal; seeFig. 5. The signal being almost independent of for frequen-cies less than few gigahertz, the noise can be filtered out for all

with GHz. The denoising filter gain shouldtherefore equal 0 inside (1,5 GHz) and equal 1 outside.

The practical implementation of this filter requires specificdevelopments, such as discretization, that are not reported herefor the sake of brevity, see [10] for a full description.

The result of applying this filter is shown in Fig. 6. It can beseen that the three clutter bands are effectively filtered out andthat the hyperbolic signatures of the landmines are not distorted.

This clutter reduction method is well adapted to GPR data.It reduces significantly clutter without distorting too much thelandmine signatures. Moreover, its implementation is simple


and its computational cost is low. The next section presents theabrupt change detection algorithm. Results obtained by com-bining clutter reduction and the abrupt change detector are pre-sented in Section IV.

III. ONLINE ABRUPT CHANGE DETECTION ALGORITHM

Due to the significant variability of landmine signatures, it isquite difficult to implement model-based detection. Indeed anaccurate and tractable GPR signals model is very hard to definesince it requires accurate modeling of electromagnetic wavespropagation into an heterogeneous soil. A natural model-freeapproach relies on the following remark: the landmine detectionproblem can be cast into an abrupt change detection problemin Bscans. The following subsection presents a model-free ap-proach to abrupt change detection, initially introduced in [11]for time series segmentation and formulated here for Bscanslandmine detection.

A. Model-Free Abrupt Change Detection

The online abrupt changes detection problem can be statedas follows [11]. Assume that data in a space are extractedonline from a signal. In our case the word signal refers to ei-ther the horizontal axis or the vertical axis of Bscans; see Sec-tion IV. Considering an analysis point where is some index,and two data subsets: x and x

.Let x x be a dissimilarity measure between the sets

x and x . The abrupt change detection problem can be cast asfollows [9], [11]:

• Hypothesis : x x (no abrupt change occurs)• Hypothesis : x x (an abrupt change occurs)

where is a threshold that tunes the sensibilty/robustnesstradeoff, as in every detection problem. The detection perfor-mance is thus highly dependent on the dissimilarity measure

selected. In this section, the dissimilarity measureis built from the so-called level sets of x and x . The

-support vector ( -SV) level set estimation technique can beused to estimate the density support of an unknown probabilitydensity function.

B. -SV Level Set Estimation

Support vector level set estimation, also referred to as noveltydetection and single class classification is a specific SVM algo-rithm. Assuming that a set of training vectors xis available in an input space denoted , the aim of SV level setestimation is to find a region x of where most of the data

lie. The region x must also have minimumvolume. A good insight about the theory of level set estimationcan be found in [12] and [13].

In the SV level set approach, the estimation of x is equiv-alently addressed by estimating a decision function x suchthat x if x and x otherwise.

In order to present the SV approach, a mapping from toa so-called feature space is defined

(3)

Fig. 7. Training vectors mapped by� lie on a hypersphere of radius 1 in featurespace F .

It is assumed that is endowed with a dot product in. This dot product, together with the mapping , defines a

kernel over by

(4)

Furthermore, we assume that is normalized such thatfor any in . Hence, for all

. In other words, the mapped inputspace is a subset of the hypersphere with radius onecentered at the origin of , denoted . The training vectors aremapped in and lie in as shown in Fig. 7. The SV approach tosingle-class classification consists of positioning a hyperplane

in such that the origin is located on one side ofwhereas most of the training vectors are located on the oppositeside. The equation of a hyperplane in is given by

where and are elements of and . Choosingis equivalent to choosing the corresponding decision functionx with and . x defines the

segment of the hypersphere in where x is positive, thatis to say the level set of x.

We do not require that all training vectors lie in the segmentof the hypersphere where , because some ofthe training vectors might be outliers that are not representativeof the data considered [12].

In standard SV level set estimation, it is shown that the abovegeometrical problem in can be formulated as the followingoptimization problem:

(5)

subject to and where is a positiveparameter that tunes the possible amount of out-liers and are the so-called slack variables, which allow someoutliers.

This constrained optimization problem admits the followingdual formulation in terms of Lagrange multipliers [12]:

(6)


subject to

for and

The hyperplane decision function can be written as

(7)

The amount of outliers in the training set x is controlled bythe parameter . It is shown in [12] that is an upper bound onthe fraction of outliers in the training set x.

In practice, we do not explicitly define the mapping ; we de-fine instead the kernel . Provided is positive defi-nite (i.e., it verifies Mercer conditions [12]), it implicitly definesa mapping , a feature space , and its dot product. Here weconsider the Gaussian kernel with parameter

(8)

The following subsection builds a change detection algorithmon SV level set estimation.

C. Online Kernel Change Detection Algorithm

Consider, at analysis point , the two training sets xand x of size

and . Then, train a SV level set estimator from the training setx and train independently another SV level set estimator fromthe training set x . Therefore, two optimal hyperplanes and

parametered respectively by and are derived.Going back to and define the region x and x . Ifan abrupt change occurs at index , it is expected that the region

x and x do not strongly overlap.In order to measure the overlap of x and x , a contrast

measure is computed at each index reflects the dis-similarity between the sets x and x via a dissimilarity measurecomputed between x and x ; see [14].

In feature space , the hyperplane defines a segment ofthe hypersphere denoted , and similarly, the hyperplane

defines ; see Fig. 8. is a measure of the distance inbetween and , computed as the arc distance between the

center of and , divided by the sum of the radii of and. The contrast measure is computed in feature space,

but it informs about the similarity of x and x in . It iscomputed thanks to the kernel described in Section III-B;see [14].

Abrupt changes are finally detected whenever the indexis larger than a threshold . This means that x and x aresignificantly different as proved in [11]. Once is computed,the training sets x and x are updated at analysis point .

An important remark is that the SV level set estimator trainingis trained twice at each analysis point , as level set estimationis performed over both x and x training sets. A noncompu-tationally efficient procedure would consist in recomputing the

Fig. 8. Feature space representation of the regions Rx and Rx . They arerepresented as segments of hypersphere� and� . The index I(a) is computedas d (c ; c )=(r + r ) where d is the arc distance on the hypersphere.

parameters from scratch using the optimiza-tion problem of (6). Instead, the parameters andare also updated using the online -SV novelty detection tech-nique presented in [15]. This makes the computational cost ofthe algorithm low.

The following section presents the full landmine detectionand localization method.

IV. LANDMINE DETECTION METHOD

As explained in Section II, clutter appears in Bscans ashorizontal stripes, and a buried object appears as a hyperbolicspreading function resulting from the imperfect directivity ofthe GPR antenna; see Fig. 2. Hence, the two hyperbola branchesresulting from a buried object are much more energetic thanthe background (response of the soil without buried objects).It is thus quite natural to adopt an abrupt change frameworkwhen moving along the horizontal or vertical axis of a Bscan.The landmine detection method proposed consists of detectingall abrupt changes in Bscan data, along both the time andspatial dimensions. The following step consists of finding thosecoming from the responses of buried landmines. Informationrelative to the depth and the position of landmines can thereforebe extracted. More precisely the two steps of our procedure areas follows.

• Step 1: Spatial abrupt changes are searched in order todetect the possible horizontal landmines position. Clutterreduction is not necessary here as it is constant along thehorizontal Bscan axis.

• Step 2: The time abrupt changes are searched in order todetect the buried objects response times. Clutter has tobe removed beforehand in order to avoid detecting clutterbands instead of real landmines.

The precise description of these steps is given in Sec-tions IV-A and IV-B below.

A. Description of Step 1

Data used in the abrupt change detection algorithm to formthe training sets are extracted from a Bscan as follows. Eachvector is made of one Ascan. At each horizontal position(thus ), two training sets x and x made of respectively


Fig. 9. Block diagram of Step 1.

and Ascans are built (typical values are ; seeSection V-A). Note that the abrupt changes detection algorithmcan be implemented online along the spatial coordinates . (Inother words, it is not necessary to have the entire Bscan to searchthe abrupt changes.) The contrast measure is computed asexplained in Section III-C. Spatial abrupt changes are detectedwhenever the index is larger than a threshold which isdetermined heuristically (this means that the two training setsdiffer significantly and that there might be a buried object or alandmine). Once is computed, the training sets x and xare updated at spatial coordinate by incorporating the nextAscan in x , removing the oldest Ascan from x , and so forth.A block diagram of Step 1 is represented in Fig. 9.

A buried object is characterized by two near abrupt changeswhich indicate the boundaries of the object in a Bscan and thustheir horizontal positions. It is important to notice that Step 2 isonly applied if at least one buried object is detected on a Bscanat Step 1.

B. Description of Step 2

Let us consider a sub-Bscan made of consecutive Ascanstaken from a Bscan containing the response of one buried ob-ject detected at Step 1. The sub-Bscan is first preprocessed bythe clutter reduction method described in Section II. Then, atime-varying gain (TVG) is applied to the filtered Bscan in orderto compensate the spreading losses and the losses due to thepropagation through a lossy soil; see [16].

Data made of one Bscan line are extracted from the prepro-cessed sub-Bscan. At each time instant (thus ), twotraining sets x and x made of respectively and Bscanlines are built (typical values are ; see Sec-tion V-A). The contrast measure is computed by the abruptchange detection algorithm. Time abrupt changes are detectedwhenever the index is larger than a threshold which

Fig. 10. Block diagram of Step 2.

is determined heuristically. Once is computed, the twotraining sets are updated at time instant .

Numerous abrupt changes are likely to be detected due tothe multiple reflections between buried objects and GPR an-tenna and also due to clutter reduction residues. Therefore, itcan be difficult to determine automatically the response time ofthe landmines. This is the reason why the contrast measureis replaced by in order to introduce a weight on each abruptchange. At each time instant is expressed as the productof the contrast measure by the weight function asfollows:

(9)

The weight function is built as follows. The preprocessedsub-Bscan is normalized, and a thresholding on the magnitudeis applied. (Thresholding is often used to suppress noise in situ-ations where the signal-to-noise ratio is large, i.e., after a goodclutter reduction.) All magnitudes below the chosen thresholdare set to zero. Then the first-order time derivative of each Ascanof a Bscan is computed. The weight function, denoted ,is expressed as the mean over all these time derivatives. It in-dicates how the magnitude of the signal varies with respect totime. Hence, by multiplying by at each time instant,this leads in enhancing significantly the magnitude of the abruptchanges due to landmines in comparison with those comingfrom clutter residuals. A block diagram of Step 2 is representedin Fig. 10.


Fig. 11. Simulated Bscan containing the responses of two buried objects.One is situated at horizontal positions f0:6; 0:9g and buried at 8 cm from theair–ground interface. The other is situated at horizontal positions f2:1; 2:4gand buried at 3 cm.

TABLE ISIMULATION PARAMETERS

C. Tuning the Algorithm

The tuning of and is generally imposed by the dy-namics of the signal. Small and make the algorithm detectfrequent, small changes, whereas large and enable the de-tection of long-term changes. In the landmine detection frame-work, the emitted pulse is of short duration (few nanoseconds).Therefore, we need to detect small changes in the return signal.Here, we choose . The kernel parameter influ-ences the location of the vectors on the hypersphere . shouldbe chosen greater than one. The rate of outliers is tuned ac-cording to detection requirements: for values about 0.2 to 0.8,the influence of outliers is limited, which reduces the rate offalse alarms. Finally, there is no automatic tuning for the thresh-olds and . An analysis of simulation results on a Bscan con-taining a landmine response is used to select and . For moredetails on the tuning of the algorithm parameters, see [11].

V. SIMULATIONS

A. Simulated Data

The method described previously is applied to the simulatedBscan shown in Fig. 11. The split step 2-D method presented in[17] is used to generate this Bscan. An electromagnetic (EM)pulse, modeled by a Gaussian function, is sent at a height of11 cm above an homogeneous ground in which two objectshave been placed. The central frequency of the pulse spectrumis 900 MHz. To apply the split step method the relative dielec-tric permittivity and the quality factor of the soil andobjects must be known. The coupling effects between antennasthat arise for bistatic GPR are not taken into account. The sim-ulation parameters are given in Table I.

Fig. 12. Contrast measure I (l) for the Bscan of Fig. 11. Two buried objectsare detected. One is situated between horizontal positions l = 0:63 and l =0:85 and the other one between l = 2:1 and l = 2:37.

As the objects positions and their physical parameters areknown, their response times can be computed by

where is the EM wave propagation speed in the air, the radarheight, the object depth, and the soil relative dielectricpermittivity. Hence, the real horizontal positions and responsetimes of the buried objects are known and can be compared withthe one found by applying the landmine detection method pro-posed in Section IV.

Step 1: The detection of the objects horizontal positions iscarried out. Data made of 1 Ascan are extracted from the Bscan.The training sets sizes are . The Gaussian kernelparameter equals 10, and the amount of outliers which is tunedby equals 0.5. Fig. 12 displays the index which is com-puted by applying the abrupt changes detection algorithm. Bychoosing heuristically the threshold , the horizontalabrupt changes due to the returns from the two objects are cor-rectly detected; see Fig. 12. The first object is situated betweenthe two first abrupt changes detected, at horizontal positions

, and the second object is situated betweenthe two next abrupt changes detected, at horizontal positions

of the Bscan.Step 2: The responses time of the two detected objects

are searched. The interest is given to the detection of theresponse time of the object situated at horizontal positions

. As a consequence, the algorithm is notapplied to the entire Bscan but to the sub-Bscan which is madeof the Ascans data recorded at positions . Thissub-Bscan is preprocessed as explained in Section IV-B; seeFig. 13. The data extracted from the preprocessed sub-Bscanare made of 1 line of this sub-Bscan. The training sets sizesare . The Gaussian kernel parameter is ,and the amount of outliers is tuned by . It can beseen in Fig. 14 that all the abrupt changes occurring in timeare correctly identified. However, it is almost impossible todetect automatically the one coming from the object. This isthe reason why a new index has been built in order to adda weight on each abrupt change.


Fig. 13. Preprocessed sub-Bscan containing the response of the objectdetected at horizontal positions l = f0:63; . . . ; 0:85g.

Fig. 14. Normalized contrast measure I (t) for the sub-Bscan of Fig. 13.

Fig. 15. Normalized contrast measure ~I (t) for the sub-Bscan of Fig. 13.

Fig. 15 displays the index with . The abruptchange detected is the one due to the returns of the object.Hence, the response time is determined: ns. The realburied object response time is ns. This leads to anerror on the depth of 3.5 mm, which is acceptable.

Fig. 16. Preprocessed sub-Bscan containing the response of the objectdetected at horizontal positions l = f2:1; . . . ; 2:37g.

Fig. 17. Normalized contrast measure ~I (t) for the sub-Bscan of Fig. 16.

The same procedure is applied to the preprocessed sub-Bscandisplayed in Fig. 16 to determine the time response of the objectsituated at lateral positions . As shown inFig. 17 the peak of maximum amplitude is detecting at timeinstant ns, and it corresponds to the time responseof this object which is theoretically equal to 1.33 ns. The erroron depth is of 0.5 mm and thus can be neglected.

The proposed detection method is efficient to detect the re-sponse times and the horizontal positions of buried objects onsimulated Bscan. The method can now be tested on real datarecorded in the frame of landmine detection.

B. Real Data

The landmine detection method is applied to the real Bscanshown in Fig. 18. It results from the recording of a GPR movedalong a survey line above an earthy soil in which two MAUS1landmines of metallic content were buried. One was buried at adepth of 5 cm, and the other one was laid down on the groundsurface. The pulse GPR used operates at 1 GHz.

The parameters for the abrupt changes detection algorithmare the same than for Step 1 of Section V-A. Fig. 19 displays


Fig. 18. Bscan recorded above an earthy soil. Two MAUS1 landminesresponses are recorded. One is coming from a MAUS1 landmine buried at adepth of 5 cm (left arrow) and the other one from a MAUS1 landmine laiddown on the ground surface (right arrow).


the contrast measure with a threshold . The hori-zontal abrupt changes due to the returns from the two mines arecorrectly detected. The first mine is situated at horizontal posi-tions , and the second mine is situated athorizontal positions of the Bscan.

The Bscan is divided into two sub-Bscans which are bothpreprocessed. Each sub-Bscan contains the response of a land-mine; see respectively Figs. 20 and 22. The abrupt change de-tection algorithm, with the same parameters than for Step 2 ofSection V-A, is applied to each sub-Bscan independently, anda contrast measure is computed for each of them. Resultsare displayed in Figs. 21 and 23.

As it can be seen on the Bscan displayed in Fig. 20, there isresidual clutter. However, the abrupt changes detected using thenew index with a threshold correspond to thelandmine. Its time response equals ns; see Fig. 21.In Fig. 22, multiple reflections between the radar antennas andthe landmine laid on the ground are recorded. The response time

Fig. 20. Preprocessed sub-Bscan containing the response of the landminedetected at horizontal positions l = f0:15; . . . ; 0:25g.

Fig. 21. Normalized contrast measure ~I (t) for the sub-Bscan of Fig. 20. Thetime response t of the landmine is detected at 3.25 ns.

Fig. 22. Preprocessed sub-Bscan containing the response of the landminedetected at horizontal positions l = f0:54; . . . ; 0:66g.

of the landmine is, however, well detected and is equal tons; see Fig. 23.


Fig. 23. Normalized contrast measure ~I (t) for the sub-Bscan of Fig. 22. Thetime response t of the landmine is detected at 2.35 ns.

Fig. 24. Bscan recorded above an earthy soil. The responses coming from anAUPS landmine (left arrow) and from a MAUS1 landmine (right arrow) buriedboth at 1 cm are recorded.

The proposed detection method is now tested on the Bscandisplayed in Fig. 24. It results from the recording of a GPRmoved along a survey line above an earthy soil in which anAUPS landmine of very low metal content was buried at a depthof 1 cm and a MAUS1 one of metal content was buried at thesame depth.

Fig. 25 displays the index . The horizontal abruptchanges due to the returns from the two mines are correctlydetected. The first mine is situated at horizontal positions

, and the second mine is situated at hori-zontal positions of the Bscan. The Bscanis then preprocessed and divided into two subimages each ofthem containing the response of a landmine. The abrupt changedetection algorithm is then applied to each subimage, and thecontrast measure is computed at each time.

As shown in Fig. 26, the time response of the AUPS land-mine is correctly detected and equals ns. The timeresponse of the MAUS1 landmine is also correctly determined:

ns; see Fig. 27.


Fig. 26. Normalized contrast measure ~I (t) for the AUPS landmine. The timeresponse t of the landmine is detected at 3.7 ns.

Fig. 27. Normalized contrast measure ~I (t) for the MAUS1 landmine. Thetime response t of the landmine is detected at 3.8 ns.

C. Performances Analysis of the Landmine Detection Method

The performance of our landmine detection method is studiedin terms of detection probability and false alarm probability withthe help of receiver-operating characteristic (ROC) curves.


Fig. 28. Laying configurations of the landmines.

For this, a set of real Bscan data that have been collected bya bench arc are used. The bench allows scanning, line by line inthe abscissa direction, of a -m area of the ground witha 2-cm step in both directions, i.e., an amount of by

Ascans for the scanned area. By concatenating all theAscans in the direction, we obtain a set of Bscan data.The laying configuration of the landmines is shown in Fig. 28.Five MAUS1 landmines and one AUPS landmine have beenburied at different depths in an agricultural soil without any po-tential other objects such as twigs or rocks.

For each Bscan, we apply Step 1 of our detection method. Aburied object is characterized by two near abrupt changes whichindicate the boundaries of the object in a Bscan and thus theirhorizontal positions. For each detected horizontal position, if itis a theoretical position of a landmine, the detection is true. Oth-erwise it is considered as a false alarm. Then, the probability ofdetection and the probability of false alarm are computed fordifferent values of the threshold . The corre-sponding ROC curve is plotted in Fig. 29. For , the prob-ability of detection is maximum and is equal to 0.8, while theprobability of false alarm is equal to 0.25. Hence, the horizontalpositions of the buried landmines are not all detected. Indeed,most of the horizontal positions of the AUPS landmine are notdetected. This can be explained by the fact that this landminehas a very low metal content and that its response on a Bscan isnot characterized by a hyperbola but by a thin horizontal stripe;see Fig. 24. Hence, few abrupt changes due to this landmine arelikely to occur in the set of Bscans data.

Then, for each Bscan of Step 1 where buried landmineshave been detected, we apply Step 2 of our landmine detectionmethod. After clutter reduction, each of these Bscans is splitinto two sub-Bscans so as to find the response times of thelandmines. For each sub-Bscan, made of the Ascans recordedat positions , abrupt changes in the time directionare searched. A buried object is characterized by two nearabrupt changes in the time direction which are relative to theresponse times of the top and bottom of the object. Hence, for

Fig. 29. ROC curve for Step 1.

Fig. 30. ROC curve for Step 2 for landmines whose horizontal positions areinside [0; 0:5].

each detected response time, if it corresponds to a theoreticalresponse time of a landmine, the detection is true. Otherwise itis considered as a false alarm. Then, the probability of detectionand the probability of false alarm are computed for differentvalues of the threshold . The correspondingROC curve is plotted in Fig. 30. For , the probability ofdetection is maximum and is equal to 0.99 while the probabilityof false alarm is equal to 0.19. Finally, the same method isapplied to each sub-Bscan, made of the Ascans recorded atpositions . The corresponding ROC curve is plottedin Fig. 31. For , the probability of detection is maximumand is equal to 0.95 while the probability of false alarm is equalto 0.07. It can be seen that the probability of false alarm isgreater for the landmines buried in area 1 thanfor the one situated in area 2 . This can be ex-plained by the fact that in area 1 the three landmines are buriedat greater depths than the ones situated in area 2. Hence, theirresponses are more attenuated and might even be sometimesless energetic than the ones from clutter residues. Moreover,in area 1 there is an AUPS landmine whose signature is notan hyperbola but a thin horizontal band. The digital filter weuse for clutter reduction is built in order to filter the horizontalstripes that represent the clutter in a Bscan. As a consequence,a part of this landmine response is filtered.


Fig. 31. ROC curve for Step 2 for landmines whose horizontal positions areinside [0:5; 1].

VI. CONCLUSION

The abrupt change detection algorithm is a very promisingtool in the frame of landmine detection. The different simu-lation results on simulated and real data show its abilities todetect automatically the horizontal positions and the time re-sponses of buried objects whose signatures are hyperbolic. Ithas been shown thanks to the simulations that this algorithm isable to detect landmines buried at different depths and whosemetal content can be low. However, its performance is relatedto the efficiency of the clutter reduction method used as thesignal-to-noise ratio must be large. The digital filter used hereinfor the clutter reduction is efficient as it reduces significantly theclutter without bringing distortions to the hyperbolic signaturesof the buried objects.

The advantage of the proposed landmine detection methodon others [2]–[8] is that it does not require a physical or a sta-tistical model of a landmine signal. Moreover this method is ro-bust since the SV level set estimators used in order to build thecontrast measure allows outliers in the training sets; seeSection III-B. The disadvantage of our approach is that buriedobjects are detected but no information on their nature is given.Other sensors such as metal detectors and infrared cameras canhelp to discriminate the detected objects. Multisensor fusionmethods for landmine detection, such as the ones proposed in[18] and [19], can then be used.

The performances of our landmine detection method havebeen evaluated thanks to the computation of ROC curves on aset of real Bscan data. This study shows the good potential ofour detection method. We are currently working at collectingsimulated data as well as real data, for different laying config-urations of the landmines, in order to deepen the performancesstudy of the proposed landmine detection method.

ACKNOWLEDGMENT

The authors are grateful to the Fondation Norbert Ségard forits financial contribution. The authors would like to thank thereviewers for their comments and suggestions to improve thequality of this paper.

REFERENCES

[1] D. J. Daniels, Ground Penetrating Radar, 2nd ed, ser. IEE Radar, Sonarand Navigation #15. London, U.K.: Inst. Elec. Eng., 2004.

[2] P. D. Gader, J. M. Keller, and B. Nelson, “Recognition technology forthe detection of buried landmines,” IEEE Trans. Fuzzy Syst., vol. 9, no.l, pp. 31–43, Feb. 2001.

[3] Q. Zhu and L. M. Collins, “Application of feature extraction methodsfor landmine detection using the Wichmann/Niitek ground penetratingradar,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 1, pp. 81–85, Jan.2005.

[4] P. D. Gader, M. Mystkowski, and Y. Zhao, “Landmine detection withground penetrating radar using hidden Markov models,” IEEE Trans.Geosci. Remote Sens., vol. 39, no. 6, pp. 1231–1244, Jun. 2001.

[5] E. Bermani, A. Boni, and A. Massa, “An innovative real-time techniquefor buried object detection,” IEEE Trans. Geosci. Remote Sens., vol. 41,no. 4, pp. 927–931, Apr. 2003.

[6] K. C. HO and P. D. Gader, “A linear prediction landmine detection al-gorithm for hand held ground penetrating radar,” IEEE Trans. Geosci.Remote Sens., vol. 40, no. 6, pp. 1374–1384, Jun. 2002.

[7] S. Yu, K. Mehra, and T. R. Witten, “Automatic mine detection basedon ground penetrating radar,” in Proc. SPIE, vol. 3710, Apr. 1999, pp.961–972.

[8] X. Xu, E. L. Miller, C. M. Rappaport, and G. D. Sower, “Statisticalmethod to detect subsurface objects using array ground-penetratingradar data,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 4, pp.963–976, Apr. 2002.

[9] M. Basseville and I. Nikiforov, Detection of Abrupt Changes—Theoryand Application. Upper Saddle River, NJ: Prentice-Hall, 1993.

[10] P. Vanheeghe, E. Duflos, and D. Potin, “Landmines ground penetratingradar signal enhancement by digital filtering,” IEEE Trans. Geosci. Re-mote Sens., Jan. 2005, submitted for publication.

[11] F. Desobry and M. Davy, “An online kernel change detection algorithm,”IEEE Trans. Signal Process., 2006, submitted for publication.

[12] A. Smola and B. Schölkopf, Learning with Kernels. Cambridge, MA:MIT Press, 2002.

[13] S. Mika, K. S. Müller, G. Rätsch, K. Tsuda, and B. Schölkopf, “An intro-duction to kernel-based learning algorithms,” IEEE Trans. Neural Netw.,vol. 12, no. 2, pp. 181–201, Mar. 2001.

[14] F. Desobry and M. Davy, “Dissimilarity measures in feature space,” inProc. IEEE ICASSP, Montreal, QC, Canada, 2004, pp. 473–476.

[15] A. Gretton and F. Desobry, “Online one-class nu-SVM, an application tosignal segmentation,” in Proc. IEEE ICASSP, Hong Kong, China, Apr.2003, pp. 709–712.

[16] N. Milisavljevic, “Analyse et fusion par la methode des fonctions decroyances et données multisensorielles pour la détection de minesantipersonnel,” Ph.D. thesis, Ecole Nat. Supérieure des Télécommun.,Paris, France, 2001.

[17] A. Bitri and G. Grandjean, “Frequency-wavenumber modeling and mi-gration of 2D GPR data in moderately heterogenous dispersive media,”Geophysics, vol. 46, pp. 287–301, 1998.

[18] S. Perrin, E. Duflos, P. Vanheeghe, and A. Bibaut, “Multisensor fusionin the frame of evidence theory for landmines detection,” IEEE Trans.Syst., Man, Cybernet., vol. 34, no. 4, pp. 485–498, Nov. 2004.

[19] N. Milisavljevic and I. Bloch, “Sensor fusion in anti-personnel mine de-tection using a two-level belief function model,” IEEE Trans. Syst., Man,Cybernet. C: Appl. Rev., vol. 33, no. 2, pp. 269–283, May 2003.

Delphine Potin was born in Liévin, France, onSeptember 12, 1979. She received the engineer degreefrom the Institut Supérieur d’Electronique du Nord(ISEN), Lille, France, and the M.Sc. degree from theUniversity of Manchester Institute of Science andTechnology, Manchester, U.K., both in 2002. She iscurrently pursuing the Ph.D. degree at the Laboratoired’Automatique, Génie Informatique and Signal(LAGIS UMR CNRS), Villeneuve d’Ascq, France.

Her current research activity deals with personnellandmine detection.


Philippe Vanheeghe (M’92–SM’97) was born inFrance on July 20, 1956. He received the M.S.degree in data processing, the Diploma of AdvancedStudy in data processing, the Ph.D. degree, and theHabilitation à Diriger des Recherches (HDR) allfrom the University of Lille, Lille, France, in 1981,1982, 1984, and 1996, respectively.

He is currently a Professor at the Ecole Centralede Lille, Lille. He was an Assistant Professor at theInstitut Supérieur d’Electronique du Nord, Lille, andwas promoted to Head of the Signals and Systems

Department of the same institution in 1990. He is the Head of the Labora-toire d’Automatique, Génie Informatique et Signal (LAGIS UMR CNRS), Vil-leneuve d’Ascq, France. His research activities include multisensor manage-ment, signal processing, signals, and systems modeling. He is a coauthor withProf. E. Duflos of several papers about guidance law modeling, multisensormanagement systems with application to radar sensor management, and per-sonnel landmine detection.

Dr. Vanheeghe has been a Member of the International Program Committee(IPC) of several international symposiums (IEEE, IMACS) and has acted as asession organizer for many international conferences.

Emmanuel Duflos (M’00) was born in Amiens,France, on June 20, 1968. He received the engineerdegree from the Institut Supérieur d’Electroniquedu Nord (ISEN), Lille, France, the Diploma ofAdvanced Study in automatic control and signalprocessing from the University of Paris XI, Orsay,France, the Ph.D. degree in engineer sciences fromthe Université de Toulon et du Var, Toulon, France,and the Habilitation à Diriger des Recherches (HDR)from the University of Lille I, Lille, in 1991, 1992,1995, and 2003, respectively.

He is currently a Professor with the Ecole Centrale de Lille, Laboratoire d’Au-tomatique, Génie Informatique et Signal, Villeneuve d’Ascq, France, after eightyears at ISEN. His current research activity deals with multisensor systems fromsignal analysis for data fusion to multisensor management in moving multitargetenvironment. He is coauthor with Prof. P. Vanheeghe of several papers aboutguidance law modeling, multisensor management systems with application toradar sensor management, and personnel landmine detection.

Manuel Davy was born in Caen, France, in 1972.He graduated in Engineering and received thePh.D. degree in signal processing from the Univer-sity of Nantes, Nantes, France, in 1996 and 2000,respectively.

He was a Research Associate at the University ofCambridge from 2000 to 2002, and he is currently afull time Researcher at the French National ResearchCenter (CNRS), Laboratoire d’Automatique, GénieInformatique et Signal, Villeneuve d’Ascq, France,His research interests are signal processing, Bayesian

statistics, machine learning, and audio signals.

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