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Anti-Personnel Landmine Detection Based on

GPR and IR Imaging: A Review

Alauddin Bhuiyan and Baikunth Nath

Computer Science and Software Engineering,

The University of Melbourne, Melbourne, Australia 3010

Abstract: Ground penetrating radar (GPR) and Infrared (IR) camera have become two established

sensors for detecting buried anti-personnel mines (APM) which contain no or a little metal. This report

reviews the detection techniques of APM using GPR and IR, and describes particular situations where

each technique is feasible. We provide an analysis for fusion based detection and classification of APM.

We discuss the GPR and IR data acquisition, signal processing and image processing methods. We also

include a comparative study of these two sensors with respect to signal processing and target detection

procedures. The report discusses the strengths and weaknesses of each of the sensors based on data

capturing efficiency, overcoming environmental difficulties and sensor technology. Finally, we

emphasize that a geometrical feature based sensor fusion, combining GPR and IR, for detection and

classification of APM may be the most effective technique.

Keywords: Ground Penetrating Radar, Infrared Imaging, Mine Detection, Sensor Fusion

1. Introduction

Landmines are causing enormous problems (for example, they endanger life and make the land

uninhabitable) in a large number of areas throughout the world. Based on the estimation of International

Red Cross, there are about ten billion mines buried in eighty countries [1]. Detecting and classifying

minimum-metal/non-metal mines buried in soil offers considerable challenges. The insidious nature of

mines has stimulated significant research – spanning over more than half a century – on techniques for

detecting and identifying the buried-mines [2]. The most significant tools used for mine detection are

GPR and IR camera.

There are mainly two types of landmines, anti-tank mines (ATM) and anti-personnel mines (APM), which

kill or maim people around the world. ATM is usually large in size and contains metal, so it can be

detected by conventional metal detectors. APM on the other hand is smaller in size (60-120mm in

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diameter and 40-70mm in thickness) (Fig.1). APM are mostly made of plastic making them more difficult

to detect since the dielectric properties of their plastic content are similar to soil. For this reason, the

reflected signal from the mine is usually weak and is masked by its background.

Most mine detection techniques, based on sensor data and image, consist of signal processing, image

processing and decision processes. In this report, we will focus our discussion on GPR and IR data

acquisition, signal processing and image processing techniques and classification methods for APM. We

provide a comparison between the two techniques for justifying their fusion. The proposed GPR and IR

geometrical feature level fusion is likely to provide better detection and classification of APM.

Fig.1. Typical Anti-personnel mines OZM-4 (left) and PMD-6 (right)

In recent studies [3-6], single/hybrid sensors have achieved significant improvement on APM detection.

However, there are limitations on soil conditions, ground penetration depth, false alarm rate, classification

and orientation of the mine. Classification and orientation of the buried mine is very important for safe

and efficient demining. To determine correct classification and orientation we must require better imaging

technique based on GPR and IR sensors.

For optimum performance, an understanding of the limitations of the sensors induced by the environment

like soil type, soil structure, humidity, temperature and vegetation is necessary. Signal processing is the

most essential part for GPR data as is the image preprocessing for IR. For a proper analysis of the image

to help classify the target, understanding of image processing is essential for both GPR and IR techniques.

We discuss particular situations where the two techniques can be most useful and emphasize that no

single sensor has the potential to increase mine detection probability and decrease the false alarm rate for

all types of mines. Here, we propose a multi-sensor fusion system framework to combine both GPR and

IR techniques. This proposed system is likely to decrease the mine detection time while maintaining high

probability of mine detection.

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This rest of this article is organized as follows. In Section 2, GPR based APM detection techniques are

described. The use of IR imaging for APM detection is given in Section 3. The two techniques are

compared in Section 4 to provide the reasons for proposing sensor fusion. In Section 5, we review some

of the existing sensor fusion techniques, and provide geometrical feature based sensor fusion model.

Finally, conclusions on the fusion model are drawn in Section 6.

2. Ground Penetrating Radar Techniques

In this Section we describe the GPR system, GPR data acquisition method and data processing

techniques. Using GPR data we construct image for use in detecting APM. We also discuss some of the

signal processing techniques on GPR data, and image processing for enhancing the constructed image to

facilitate target detection.

2.1. GPR systems

GPR consists of an active sensor, which emits electromagnetic (EM) waves through a wideband antenna

(Transmitter) and collects signals reflected from its surroundings (Receiver). The commonly used

frequency band of the GPR, EM wave is between 100 MHz and 100 GHz [7]. However, for APM detection

a center frequency of 1 to 2 GHz seem to be a good choice for most types of soil [8]. GPR imaging from

data is based on three most important parameters which are the wave speed, polarization and amplitude of

the propagating electromagnetic wave field. The height of the antenna above the surface is also the key

factor, increasing the height influences the image quality negatively as the spatial resolution is decreased

[9].

There are two distinct types of GPR, time-domain and frequency-domain. Time domain GPR is further

divided into i) Impulse System GPR and ii) Ultra Wide Band (UWB) GPR. Impulse GPR emits a pulse

with a carrier frequency, modulated by a square envelope. Ultra Wide Band GPR emits a pulse without a

carrier frequency; this way the spectral band of the emitted energy is very large. Frequency domain GPR

is also divided into two categories such as Linear Sweep and Stepped Frequency. The frequency domain

GPR transmits a signal with a changing carrier frequency over a chosen frequency range. This carrier

frequency can be changed, either continuously (Linear Sweep), or with a fixed step (Stepped Frequency).

GPR provides information on both the existence and location of mines. The presence of an object is

detected by checking for interruption through the round trip path of the signal. While propagating from

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the transmitter towards a buried object and being scattered back to the receiver, the electromagnetic

waves of the GPR are subject to some losses. In particular, the deeper the object is buried, the higher the

losses introduced by the soil are. In order to compensate for these attenuations in function of R or, more

directly, of time t, a time varying gain (TVG) is introduced, by which a fixed gain of X dBs/s (/m) is

added to the raw signal s, so the amplified signal in time domain is [10]:

STVG(t) = s(t)10Xt/20

(1)

The distance between the sensor and an object is measured by using the time delay, �t, between the

emitting and receiving moments of the signal as

r = 2

v �t (2)

where v represents the velocity of EM wave in the medium, and r the distance of the object from the GPR

sensor. Since many parameters of EM waves, including the velocity, vary according to the content of soil,

soil parameter should be estimated prior to taking the measurement [7].

GPR detects any object below the soil surface if it differs from the surrounding medium in the

conductivity (metallic targets), the permittivity or the dielectric constant (plastic and non-conducting

targets), or the permeability (ferrous metals) [10]. The penetration depth of the wave into the soil usually

depends on two factors, the humidity of the soil and the wavelength of EM wave. The content of water in

the soil significantly reduces the depth of penetration of a wave with a relatively shorter wavelength.

Based on the reflection and penetration properties, GPR works best with low frequency EM waves in dry

sand. low frequency signals, however, tend to make low resolution maps of data, which decreases the

accuracy of mine detection [2].

2.2. GPR data acquisition

The data is generally acquired with a handheld or vehicle mounted GPR scanner by moving it along a line

over the places where the mine is buried. The scanner is moved with fairly constant velocity and traces are

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continuously recorded. The number of traces depends on the area of the minefield or the region and a

constant inline step needs to be maintained (average 1-2 cm). The positioning of the radar system is

crucial as the distortion of the image takes place due to positioning error. Although, there are some recent

developments of sensors which are detecting mines without imaging, we have mentioned earlier that

imaging is compulsory if we want to consider the orientation of the mine. The automated data acquisition

frame can be used to reduce positioning data error GPR antennas operate in transmitter and receiver pairs

to cover a wide search area [11].

A, B and C-scans: GPR data can be represented in three different forms, A-scan, B-scan, and C- scan,

according to the number of scanning dimensions. It can be defined with the 3D coordinate system, where

the x y-plane represents the ground surface and the z-axis represents the direction into (depth) the ground.

The A-scan signal is obtained by a stationary measurement after placing an antenna above a specific

position. The collected signal is presented in the form of a group of signal strength versus time delay. A-

scanned signal is a 1D signal. B-scan signal is obtained as the horizontal collection from the ensemble of

A-scans. The collected 2D signal is presented as intensity on the plane as scan width versus time delay. C-

scan signal is obtained from the ensemble of B-scans, measured by repeated line scans along the plane.

The collected C-scanned signal forms a 3-D signal [12]. In the 3-D coordinate system (Fig.2), the x and y

axes respectively represent the horizontal and the vertical positions in the plane of the target, and the z-

axis represents the depth of the target.

2.3. Preprocessing

The acquired data usually has some clutter or noise due to vegetation (e.g. grass, bushes) and the local

variations in the material in the ground. It is necessary to remove the clutter to enhance the quality of the

data. We can distinguish three kinds of clutter, which can be approached in a different way [13]. The first

type of clutter involves everything appearing on and beneath the ground. The interface between air –

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including the first layer of vegetation and the ground creates a second type of clutter, known as the

surface clutter. The third kind, the equipment clutter, is due to the imperfections of system used. Clearly,

this will vary a lot from one system to another. At present, the pre-processing step is performed in two

different ways: i) signal processing of the GPR raw data (A scan) and ii) construct an image using the

GPR raw data and then enhance this image.

Fig.2. A 3-D coordinate system defined on a section of ground.

2.3.1. Signal processing

The signal processing is performed by different filtering techniques and deconvolution algorithm. The

following signal processing techniques are commonly used by researchers in APM detection community.

These techniques are briefly described for completeness.

� Subtraction of the average A-scan. By calculating an average A-scan over the whole B-scan, and

subtracting this from each individual A-scan, one only succeeds in eliminating the perfectly horizontal

background clutter signals. This is the most widely used way to eliminate the problem of the air-ground

reflection although in many cases it is not very efficient [14].

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� Application of a horizontal high pass Butterworth filter. This filter reduces the different background

signals effectively, but it also modifies the diffraction hyperbola substantially, which reduces the

efficiency of this method [14].

� Application of the Discrete Wavelet transform. This transformation is applied in the horizontal

direction and the coefficients of the lowest octaves (which correspond to horizontal energy) are set to

zero, and then the inverse transform is applied [14].

� Windowed average subtraction. This is a version of the general average subtraction method. Here the

average A-scan is not calculated over the whole B-scan, but only over the A-scans within a window

around the signal being considered. This subtraction can adapt to slowly varying signals so that slightly

oblique surface reflection can be eliminated [14].

� Removing DC offset and DC offset drift. During data acquisition A-scans are suffered from DC

(Direct Current) offset or DC offset drift which are removed by applying second order polynomials [15].

� Application of ARMA model. A system identification based clutter removal algorithm using the

ARMA model that describes clutter. ARMA model basically considers abrupt changes between clutter

and target signal [16]. Figure 3 (taken from [2]) illustrates the changes in image features after clutter is

removed.

� Application of Wiener filter. An adaptive version of Wiener filter (adaptive means that it calculates its

parameters according to the data present locally in the image) records data which are in the form of A-

scans, B-scans and C-scans and can remove the noise for each of the forms [17, 18].

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� Adoption of basis function. The clutter reduction method is based on basis function decomposition of

the SF-GPR time-series from which the clutter and the signal are separated.

� Finite Impulse Response filter (FIR). FIR enhances the signal part of an A-scan and reduces the

background energy [18].

Fig.3. Parametric model background removal (left) and detected hyperbolas in mean-removed

background (right).

� Finite-Difference Time-Domain (FDTD) technique. The EM (Electromagnetic) field values of every

last time step and other variables are stored. Parallel implementation of the FDTD algorithm on a using

cluster of workstations (COW), which is a time domain method, produces data in the whole frequency

domain by performing only one calculation in time domain [19]. When resonance frequencies are used,

FDTD simulation can be applied to detect anti-personnel mine [20].

� Application of F-K filtering. F-K filter can eliminate the artifacts like high amplitude, large saddle

corners, low temporal frequencies and high spatial slopes of a frequency-wave-number domain. For GPR

signal processing, F-K filter is applied to remove artificial time shifts, gaps in coverage and variations in

signal characters [21].

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� Robust Linear Prediction (RLP) and Adaptive Shifted and Scaled (ASaS) algorithms. Both RLP and

ASaS are based on a flexible data model applicable to rough ground surface clutter removal algorithm [1].

� Karhunen-Loeve Transform (KLT). KLT can eliminate the strong GPR background and enhances

greatly the signal-to-noise ratio of the data [22].

� Deconvolution algorithm. Considering the received signal is a convolution of the impulse (frequency)

response of transmitter and receiver (which can be estimated beforehand), the deconvolution method is

applied to GPR data [23]. By applying deconvolution, the original signal is separated from the received

signal that leads to feature extraction of a mine.

The above mentioned signal processing techniques have been applied by different researchers on several

different occasions. Not all these techniques are optimized. Moreover, one technique can be most efficient

in a particular circumstance while may not be in another. In the next paragraph we state the most efficient

signal processing techniques based on their applications and image enhancement capability.

ARMA modeling provides quantifiable improvement in feature extraction applied to the detection of non-

metallic anti-personnel landmines. The Discrete Wavelet Transform and Windowed average subtraction

offer the best results in eliminating the various background signals and preserving the diffraction

hyperbola. The Wiener filter has been shown as one of the best filters for B-scan data processing as the

background and measurement noise get completely attenuated without distorting the signal itself [18].

2.3.2. GPR image enhancing

One of the popular methods of enhancing a GPR image of buried target is to use a simple filtered time-

domain back propagation algorithm. Then the inversion method is applied to retrieve more details of the

inhomogeneous characters of the buried object [24].

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A combination of opening and closing operations can remove noise and smooth the texture in an image.

This is called the alternating sequential filter (ASF). The ASF performs well with repetition rather than a

single operation. When the contrast between the background and mine target is not (usually) high enough,

the Morphological contrast enhancement and Histogram equalization algorithms may be applied [7].

The dielectric properties of plastic mines are similar to the soil and the reflection from the mine is usually

weak and masked by background. This is known an inverse scattering. To resolve inverse scattering

problem, the inversion method, reduced-order modeling technique, the forward-backward time-stepping

(FBTS) method and the gradient-based optimization method may be used [24].

2.4. GPR image processing and target classification

In order to get better detection ability and more accurate indication of the position and size of the buried

object, imaging algorithms may be applied on the preprocessed (contrast enhanced) images. For instance,

a diffraction stack is used in the spatial and time domain based on an estimation of an image value at a

certain position by stacking the data associated with appropriate arrival time for the specific location. It is

a flexible technique to handle irregularly positioned data [15].

The linearized forward scattering model is used to invert preprocessed data and obtain the three

dimensional distribution of the unknown dielectric permittivity contrast. There are several algorithms to

obtain a three-dimensional image from GPR data such as the fast multistage effective inversion algorithm,

advanced real-time algorithm and multi-component imaging algorithm [24]. We can apply Focusing

algorithm when the real target responses coherently sum together in the imaging process and the non-

target features do not focus coherently [25].

Mathematical morphology has been applied primarily for binary image processing. Basic of the first level

functions, such as dilation and erosion, are performed by structure elements such as shape and size. Gray-

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scale morphology can provide more complicated processing, such as gradient extraction, contrast

enhancement and region segmentation (watershed algorithm) as well as noise removal and smoothing

which are typical applications of binary morphology [7].

Target detection and classification: Target detection and classification algorithms can be applied after

either image enhancement or image processing algorithms or both. The APM detection and classification

is based on feature extraction, segmentation and matching algorithms. For feature extraction, Karhunen-

Loeve transformation (KLT) and Kitller-Young Transformation (KYT) have produced promising feature

extraction results in the mine detection area [7]. The amplitudes of the reflections from mines are used as

features for object classification. The final image shows a series of hyperbolic arcs from which mine

classification may be possible [26].

The Semi-automatic segmentation (cue-based analysis) is another option for mine classification where the

user defines the image information (cues) when a software system interprets these cues (such as distance,

vector, entropy, intensity and global image parameters) to generate a list of image processing functions.

These image processing functions finally enhance, segment and analyze the image for classification [17].

Target detection can also be performed in stages such as feature estimation, feature extraction, object

model formulation and final classification [18, 27, 28]. Geometrical features (dimensions, orientation and

center) based on morphological operations of dilations and erosion has also been used to maximize the

probability of detection [29]. Feature estimation step reduces the data into a feature vector which

represents the target. Feature extraction extracts out of the estimated feature vector only those features

that are pertinent to the classification problem. The object model formulation stage determines a set of

parametric or non-parametric models for each object class considered. The final classification step then

compares the feature vector extracted out of an unknown data sample, with the different class models to

determine the likelihood of being part of a mine object.

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3. Infrared Techniques

Infrared techniques based on Infrared Imaging and processing of those images to detect APM. Here, we

describe in detail about IR image acquisition method. We analyze different image pre-processing

techniques which are used for IR image enhancement. Finally, we provide a discussion on different image

processing and target detection techniques.

3.1. Infrared camera

The use of thermal IR technique is based on the thermal radiation contrast of objects, with respect to their

backgrounds. All objects at temperatures greater than absolute zero emit electromagnetic (EM) radiation

at wavelengths from 3µm to 100µm is referred to as the thermal IR radiation. The magnitude of spectral

radiations of an object depends on its temperature. For landmine detection, landmines are thought of as a

thermal barrier in the natural flow of the heat inside the soil, which produces a perturbation of the

expected thermal pattern on the surface. The detection of these perturbations (anomalies) will put into

evidence the presence of potential landmine targets [30].

Infrared camera detects infrared energy (heat) difference as low as 15mK and converts it into an

electronic signal, which is then processed to produce a thermal image on a video monitor and perform

temperature calculations [31, 32]. The IR camera consists of detectors which are transducers and convert

the energy of EM radiation into an electrical signal. There are two types of IR detectors, the photon

detector and the thermal detector. The photon detector or counter essentially measures the rate of quantum

absorption, whereas the thermal detector measures the rate of energy absorption [7].

The processing of images given by thermal scanners can be carried out in two ways. The first is the

dynamic approach where an image sequence is processed in one batch, and the second is the static

approach where the processing is performed on one individual image. Dynamic thermograph consists of

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processing a sequence of images of the same scene, submitted to heating or cooling (be it artificial or

natural), so that the scene parts follow different temperature curves in time due to the variability in their

specific characteristics (material, volume, shape). The use of thermal infrared (IR) sensors for landmine

detection has been of interest to researchers since the 1950s [30]. The Dynamic thermal IR technique has

been used since 1980s for nondestructive evaluation [33].

IR images can be captured by active or passive sensing method. The passive IR system senses only

natural radiation from the object, while the active IR system provides an extra heat source and receives

the artificial radiation created by the heat source.

The overall IR detection technique can be summarized by a sequence of steps. The first step is the

acquisition of the polarimetric IR image data. The next step is preprocessing polarimetric images for

contrast enhancement. Finally, image processing algorithms are applied to facilitate target detection and

classification.

3.2. Acquisition of IR image data

Infrared camera is often used in a vehicle based multi-sensor platform for landmine detection. It may also

be used in airborne or in a moving platform for taking images [34-36]. Polarimetric infrared camera based

on a moving platform can acquire images while moving at a constant speed and maintaining a specific

angle with the target area. An automated airborne system was introduced for IR data capturing with

GPS/INS for the camera positioning accuracy [35].

The polarimetric system is operated in a free running mode in which image sequences are acquired

continuously. The method is described as follows. Using a laser distance meter the distance traveled by

the platform along the measurement bridge is measured and recorded. Every finite distance interval the

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laser distance meter gives a trigger pulse. These trigger pulses are recorded along with the images. The

exact position of each image in the sequence is estimated using interpolation of the trigger pulses [37].

The detection performance of Infrared camera can be enhanced by means of a Polarization filter. By using

this filter not only the differences in thermal properties (for buried and surface-laid landmines) can be

observed but also surface properties (for surface-laid landmines) can be acquired [38]. Another significant

characteristics for Polarimetric IR is that within the visual and IR bands, polarization gives extra

information about objects and their surfaces [39].

Predicting the thermal signature (the temperature differences of soil surface with mine and without mine)

which is the key concept of the thermal imaging of a buried landmine requires knowledge of the heat flux

into the soil, the thermal properties of the landmine, the soil above the landmine and of the soil

surrounding the landmine. Inherent to the cyclic nature of the incoming solar radiation, the thermal

signatures of underground anomalies follow a cyclic pattern [40].

3.3. IR image pre-processing

IR polarization filters were introduced into thermal imaging systems for improving the low target-to-

clutter ratio in infrared scene [41]. However, when the contrast between the object and the background is

not adequate more processing is necessary and direct segmentation does not work. Some of the significant

image pre-processing algorithms are:

� Application of image calibration. Noise is added to the desired image because of the vibration of the

camera. Camera calibration is one of the simplest ways to reduce this noise. For each angle orientation a

cold and a warm calibration is performed. These two calibration sequences give the gain and offset for

every pixel. The offset correction removes the reflected image, the emission from the filter and the offset

of each pixel [37, 42].

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� Radiometric Calibration. Provides the radiometric calibrated images after which the image grey

values are provided in physical units: W/m2/sr (Watt per square metre per steradian). The Calibration

procedure using two blackbodies (one heated and one at ambient temperature) with temperature sensors

on two sides of IR camera, assuming the blackbodies has a uniform temperature over their full surface

and has an emission coefficient of 1. The acquired images, I(x,y), are calibrated using eq. 3, after the full

scan of the area has been performed [30].

Ic(x,y) = Ir1 + )(),(),(

),(),(12

12

1

rr

bbbb

bb IIyxIyxI

yxIyxI−

−

− (3)

With Ibb1(x,y) and Ibb2(x,y) are average blackbody images, for blackbody 1 and 2, respectively. Iri (i=1,2)

are the blackbodies radiances, estimated using planck’s equation (eq. 4):

Iri = λλ

λ

λ

λ

d

e

hc

ikT

hc

1

122

1

5

2

−

� (4)

Where, Ti (i=1,2) are the measured blackbodies temperature; �1 and �2 are respectively the lower and

upper bound of the wavelength of the camera. The resulting image radiance Ic(x,y), is directly related to

the radiance of the scene.

� Application of KLT and KYT. Karhunen-Loeve transformation (KLT) minimizes the object

representation error. It selects the main axis (maximum noise component of the variance) and applies the

first and second KLT transformations to reduce the noise. Use of Kitller and Young transformation

(KYT) leads to an improved mine image (Figure 4 adapted from [38]) by compensating the noise

component and finally have better discriminating ability for mine [43].

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� Gaussian filtering. The Gaussian filter or smoothing operator is a 2-D convolution operator that is

used to `blur' images. The pre-processing steps also include Gaussian filtering for removing noise in the

original image [44].

Fig.3. Infrared Image (left). Karhunen-Loeve Transformed Image (Center). Kittler-Young

transformed Image (right).

� Morphological filter. The morphological filter can efficiently remove noise by combining multiple

morphological operations, and can also provide more complicated processing such as gradient extraction,

contrast enhancement. Histogram equalization methods are also used for contrast enhancement [7].

� Application of Pixel fusion. Different IR intensity images taken from the same scene with different

orientations of a polarizing filter, and applying the pixel fusion for combining the polarization

information for image enhancements [41].

� Pre-whitening filter followed by a matched filter. By considering scaled 3-D Gaussian shapes of

mines, while modeling the colored noise as an auto-regressive process, detection of landmines is obtained

by applying a pre-whitening filter followed by a matched filter. The matched filter correlates the pre-

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whitened image sequence to the 3-D object model. Using the local maxima of the output position of the

mine is detected [45].

� Wavelet approach. In this denoising technique, the decision whether a coefficient originates from a

signal or noise is based on the comparison of its magnitude with a certain threshold [36].

� Autoregressive process. By separately estimating the parameters of the mine and the noise in an

alternating fashion, the background noise can be described and eliminated by autoregressive process [46].

3.4. IR image processing and target classification

Extracting mine-like shapes from the IR sensor image is a crucial task in the mine detection process. After

image pre-processing technique(s) the target/mine detection algorithms are applied.

One of the most popular methods for mine detection is Tophat filtering. Tophat filtering is a

morphological transform that is able to detect local maxima in an image within the region of a structuring

element. Detection performance can be improved by using more relevant information in the classification

process. One way to include this information is by means of features (size, shape and intensity) of regions

of interest (ROI) [37]. For target classification, Naïve Bayes classifier [47] and Novel classifier have been

used.

Hough transformation and Tophat filtering are used for feature-based detection of land mines to optimize

the target features. The feature combination methods based on the Mahalanobis distance and the Fisher

mapping have been used [48] for object classification.

Detection and classification of mines is also possible by perturbations on the thermal pattern of the soil

caused by the presence of buried objects. The procedure is based on the application of a thermal model of

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the soil. The presence of unexpected objects is made evident by the alterations on the expected pattern,

constituting the detection stage of the procedure. Classification is possible by assuming the thermal

behavior of mine targets is driven by the thermal properties of the mine explosive [49].

The watershed transformation is used in morphological image segmentation and considered as a

topographic region growing method. The mine targets are considered as changes in the vegetation/soil

textures. Texture parameters extraction and clustering methods are used for classification of background

(vegetation, soil, etc. ) and mine-like objects [36].

4. Comparison of GPR and IR Techniques

It is clear from the above sections that collecting data and creating image from the data requires more

effort in employing GPR than IR. GPR data needs serious pre-processing at signal processing level to

create the image. While IR does not need such type of pre-processing, rather image enhancement is

necessary which also is required for GPR to perform a better detection.

Since it is easier to interpret visual images than data, IR technique has been widely used for mine

detection [7]. On the other hand, GPR data can be controlled by an operator as well as for processing and

imaging. GPR is also another popular technique for anti-personnel mine detection.

In case of GPR, for mine size effects on image; calibration of wavelength is necessary which is

proportional to mine size. This is also true for polarization; experiments have shown that circular mine

image taken with linear polarization distort the shape of the circular mine [50]. For IR only the mine size

has effect on thermal imaging, the larger the mines size the clearer the thermal signatures.

The height of the antennae above the surface has influence on the GPR image. As the height increases the

spatial resolution decreases [9]. This ultimately causes a blurred image and increases the detection

complexity. There is no such type of predicament in case of IR imaging.

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GPR data capturing is more complicated compared to IR imaging. In GPR technique, usually an operator

performs scanning by a hand held system. Both measuring conditions and operator skills pose weaknesses

for the GPR scanning method [51]. In contrast, IR imaging is highly dependent on surrounding

environment conditions. It is even get biased by operator’s body temperature, and motion and vibration of

the camera.

Air-soil interface has a significant effect on both GPR and IR data capturing. In GPR scanning, it

introduces the air-soil interface clutter [52] and in IR imaging it causes clutter due to the uneven sunlight

absorption of the surface [53]. Rough surfaces lead to distortion in signals on GPR sensing because of the

irregular surface reflection. Since IR image is based only on the temperature measurement, soil surface

irregularity is not a strong barrier for IR imaging. Moreover, a specialized method of dual-frequency

microwave-enhanced infrared thermography (MEIT) is available to minimize the clutter, if any, due to

surface irregularities [54].

Soil moisture has strong effect on GPR signals. Experiments have shown that a slight increase in water

content in soil around non-metallic landmines improves detection in sand but not in clay [55]. On the

other hand, soil moisture has significant positive effect on thermal signature, largest when moist and

lowest for dry conditions [56].

GPR can provide object detection with shape (3D structure of the buried mine) and depth information

while thermal IR gives shape features only. The additional feature for the IR imaging is that it estimates

texture of background clutter and objects [57]. Migration algorithms (both in time and frequency domain)

are used in GPR mine detection to give an idea of the exact physical position (depth information) and

shape of the reflectors in the subsurface [14]. However, our study has shown that such a technique is not

feasible in IR imaging.

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Wind is another consideration that does not affect GPR mine detection technique but has a significant

effect on IR imaging. It has been shown that increase in wind speed decreases the strength of thermal

signature which in turn decreases the mine detection rate [40].

It is established that to detect a significant thermal signature of a mine at the surface, for most soils a

10cm is the maximum burial depth [58, 59]. For the GPR based mine detection methods there is no such

information available about the maximum burial depth. GPR high frequency signals can provide depth

information of the buried mine, but is a limitation for the IR camera technique [60].

Both GPR and IR camera techniques have some advantages and limitations and neither of them is

markedly superior to the other. It is clear that neither of them can provide effective information when

used in isolation. A hybrid model combining certain features of both the GPR and IR, we believe may

provide better performance for detection and classification of APM.

5. Fusion of GPR and IR

No single sensor technology has the capability of reaching a good detection rate while having a low false

alarm rate in all types of soil and with all types of mines. Hence it is best to use complementary sensor

technologies for mine detection and to perform an appropriate sensor fusion with them [61]. Sensor fusion

is the process in which information from different sensors is combined. This can be especially

advantageous, when sensors measure independent physical properties: weaknesses of one sensor are

compensated by inherent strengths of other sensors, resulting in good performance of the complete sensor

suite in a wide variety of conditions [62].

Three fusion architectures are defined based on the amount of processing performed on the sensor data

before fusion. These are data-level fusion, feature-level fusion and decision-level fusion [63].

21

Data-level fusion: In the data-level fusion architecture a minimal amount of processing is performed on

data from each sensor before data are combined. Processing is usually limited to calibration, alignment,

etc. which are essential to make data useful. The fusion operation in this architecture may comprise

stacking, weighted averaging or Kalman Filtering. The mine detection decisions can be made based on

the fused data or on features extracted from the fused data.

Feature-level fusion: In this architecture features extracted from the raw data of each sensor are merged

together to form a fused feature vector. Because feature vectors generally have a much lower dimension

than the corresponding raw data, the computational demand at the fusion center and the communication

bandwidth requirements can be significantly lower than those in the data-level fusion. The penalty,

however, is possibly the lower accuracy of the fused decisions, because of the loss of information

occurring during feature extraction at each sensor.

Decision-level fusion: In the decision level fusion technique the classification decision is made

individually at each sensor and then fused. A variety of techniques have been developed for decision level

fusion [58], which may be binary decisions or detection probabilities. Decision-level fusion is, in

principle, the least accurate because of the additional loss of information that occurs when sensor data are

reduced to decisions. Nonetheless, other considerations such as communication bandwidth and low

computational demand at the fusion center make the decision-level fusion attractive in situations dealing

with distributed mine detection.

The choice of a suitable fusion level depends on the available sensor types. When the sensors are alike,

one can opt for fusion at data-level. Feature-level fusion is appropriate when features obtained from

different sensors can be combined in a way that provides sufficient information for mine detection. When

the sensors are very different, decision-level fusion is more suitable and also computationally more

efficient [57].

22

The above study showed that for GPR and IR the suitable fusion methods are feature-level and decision

level. The feature-level sensor fusion was applied so far by [64] where the processes start with the regions

of interest with their features as measured by individual sensors. This includes three steps. First step is

object association where the features (object area and intensities) of objects from different sensors are

combined to form an associated object. The second step is to classify the features from these associated

objects by feature classification algorithms. The third and final step is the performance evaluation.

The decision-level sensor fusion techniques are Bayesian approaches, application of Dempster-Shafer

theory, fuzzy probabilities, rules and voting techniques [65, 66]. Here, the individual sensor results are

obtained that act as confidence values and are input to the Decision-level sensor fusion algorithms.

Decision-level sensor fusion can also exploit the GPR and IR advantages in sensing efficiently into

different layers by surface-fusion (extension of decision-level sensor fusion). Thermal infrared has the

limitations for adding information in the deep layer and GPR has difficulties suppressing clutter in the

surface layer. So, the surface-fusion should place emphasis on the IR sensor at the surface layer and at the

deep layer in GPR [57].

US Army’s new dual sensor, HSTAMIDS (handheld standoff mine detection system), which combines an

electromagnetic induction (EMI) sensor and GPR, is an example of sensor fusion tools. It shows a

significant improvement on low metal APM detection which is 93% in average (by experienced

operators) and 80% (for trainee) in a simulated mine field (had the proximity of a real mine field) [5, 6].

But it still suffers from high false alarm rate 23%. In addition, the sensor EMI and GPR are not

compliments to each other. Therefore, its performance as a sensor fusion module may be not convincing

in a real mine field.

23

MINEHOUND is another example of fusion (decision-level) developed by UK Department of

International Development and ERA technology which combines MD and GPR. These sensors can work

individually or together and provide audio output. At first, MD detects all metal threats and latter GPR

mode confirms a present of a threat. The results of the trials showed that it is achieved 100% detection of

the mines encountered and an improvement of false alarm rate of better than 5:1 compared with a basic

metal detector [3, 4]. But, the concept of fusion is invalidated if we consider only APMs (with no metal).

Again, its performance may be varied with high soil moisture, depth and mine size as MD and GPR will

not be the compliment of one another.

The above mentioned techniques of sensor fusion still need improvement and suffer from large false

alarm rate. Not any of the techniques can efficiently classify APM or provide any information about their

orientation. We are proposing the geometrical feature based fusion of GPR and IR which may eliminate

these limitations.

We are planning to set up an experiment to acquire data by non coincidental scanning of mine field by

GPR and IR sensors (implement GPS for determining the scale of the surface area). We will construct 3D

image from GPR data. From GPR 3D image we will calculate the geometrical features of APMs such as

size, shape and dimensions. The IR images will provide us the 2D features of APMs from which we will

find the size and shape features, and can be estimated the maximum and/minimum axis of the target. We

will also measure the actual size in terms of length or width if it is cylindrical shape and diameter and

perimeter if it is circular shape. The extracted common geometrical features from individual sensor will

be fused in the fusion module. The rest features (3D) along with estimate the invariant features of object

like elongation and compactness will be consider for classification of mine.

To calculate the orientation we have to rely on the GPR 3D image. At first we have to create a database

from the commonly used APMs. On the 3D image we will have to apply the image rotation operation.

24

Every time we will apply the rotation operation and compare the resulted image with our image database.

Finally, the orientation will be calculated from angular difference of matched image and the original

image (image constructed from GPR 3D data). We may also verify the orientation with the upper surface

of the buried APM from IR image (applying 2D rotation).

The overall technique which we are proposing is depicted in the following diagram:

Figure. The overall system for the detection and classification of APM.

6. Conclusion

We note that each of the methods reviewed has some limitations under certain conditions of environment

and mine type. It is, therefore, unlikely that any single technique will provide a breakthrough necessary to

substantially improve mine detection effort and time. To achieve substantial decreases in mine detection

time while maintaining high probability of detection as well as a low false alarm rate, an efficient

approach is a necessity. Rather than focusing on individual sensor operating in isolation, we should

emphasize that a design of an integrated, multi-sensor system may overcome the limitations of any single-

25

sensor technology. A multi-sensor hybrid system that combines GPR and IR should be used for achieving

a better performance in the detection and classification of APM.

7. References

[1] R. Wu, J. Liu, Q. Gao, H. Li, and B. Zhang, "Progress in the research of Ground Bounce

Removal for Landmine Detection with Ground Penetrating radar," In the proceedings of

Progress In Electromagnetic Research Symposium, pp. 1-5, 2005.

[2] S. Vitebskiy, L. Carin, and M. A. Ressler, "Ultrawideband, short-pulse Ground-

Penetrating Radar: Simulation and Measurement.," IEE transactions on geoscience and

remote sensing, vol. 35 (3), pp. 762-772, 1997.

[3] D. J. Daniels, P. Curtis, R. Amin, and N. Hunt, "MINEHOUND trials in Combodia,

Bosnia and Angola," In the Proceedings of SPIE, vol. 6217, pp. 1-10, 2006.

[4] D. J. Daniels, P. Curtis, R. Amin, and N. Hunt, "MINEHOUND PRODUCTION

DEVELOPMENT," In the Proceedings of SPIE, vol. 5794, pp. 488-494, 2005.

[5] R. C. Doheny, S. Burke, R. Cresci, P. Ngan, and R. Walls, "Handheld Standoff Detection

System (HSTAMIDS) Field Evaluation in Thailand," In the Proceedings of SPIE, vol.

5794, pp. 889-900, 2005.

[6] R. C. Doheny, S. Burke, R. Cresci, P. Ngan, R. Walls, and J. Chernoff, "Handheld

standoff mine detection system (HSTAMIDS) field evaluation in Namibia," In the

Proceedings of SPIE, vol. 6217, 2006.

[7] J. Paik, C. P. Lee, and M. A. Abidi, "Image Processing-Based Mine Detection

Techniques: A Review," Subsurface Sensing Technologies and Applications, vol. 3, pp.

153-201, 2002.

[8] C. Bruschini, B. Gros, F. Guerne, P.-Y. Piece, and O. Carmona, "Ground Penetrating

Radar and Imaging metal detector for antipersonnel mine detection," Journal of Applied

Geophysics, vol. 40, pp. 59-71, 1998.

[9] J. V. D. Kruk, "Three-dimensional GPR imaging in the horizontal wave-number domain

for different heights of source and receiver antennae," Near Surface Geophysics, vol. 2,

pp. 21-27, 2004.

[10] N. Milisavljevic, I. Bloch, S. V. d. Broek, and M. Acheroy, "Improving mine recognition

through processing and Dempester-shafer fusion of ground-penetrating radar data," The

journal of pattern recognition society, vol. 36, pp. 1233-1250, 2003.

[11] J. Groenenboom, J. V. D. Kruk, and J. H. Zeeman, "3D GPR data acquisition and the

influence of positioning errors on image quality," In the proceedings of 63rd Conference

and Technical Exhibition, Amsterdam, pp. 141-144, 11-15 June, 2001.

[12] J. Zhang and B. Nath, "Processing and Analysis of Ground Penetrating radar Landmine

detection," In the proceedings of The 17th International Conference on

Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE

2004), pp. 1204-1209, 2004.

[13] Y. Lostanlen, B. Uguen, G. Chassay, and H. Griffiths, "Characterisation of the clutter for

ultrawideband radar," In the proceedings of Radar'99, 1999

http://yves.lostanlen.free.fr/cv/pdf/radar99.pdf (last accessed on 19/Jan/06).

26

[14] L. V. Kempen and H. Sahli, "Ground penetrating radar data processing: A selective

survey of the state of art literature," Information retrieval and Interpretation Sciences,

1999 http://www.etro.vub.ac.be/Publications/technical_reports.asp (last accessed on

19/Jan/06).

[15] J. Groenenboom and A. G. Yarovoy, "Data processing for a landmine detection dedicated

GPR," UWB technology forum, 2000 http://www.uwbgroup.ru/eng/articles/process.htm

(last accessed on 06/July/2005).

[16] J. Brooks, L. V. Kempen, and H. Sahli, "A Primary Study in Adaptive Clutter Reduction

and buried Minelike Target Enhancement," In the Proceedings of the SPIE, Detection

and Remediation Technologies for Mines and Minelike Targets V, vol. 4038, pp. 1409-

1418, 2000.

[17] J. Zhang, Q. Liu, and B. Nath, "Image segmentation based on competitive learning,"

Journal of marine science and Application, vol. 3, pp. 71-74, 2004.

[18] H. Sahli, E. Nyssen, L. V. Kempen, and J. Cornelis, "Feature Extraction and

Classification methods for ultra-sonic and radar mine detection," In the Proceedings of

International Multiconference on Computational Engineering in Systems Applications

(CESA 98), vol. 4, pp. 82-87, 1998.

[19] B. Nath and J. Zhang, "4-D GPR Image based on FDDT parallel technique," In the

Proceedings (CD ROM) of the conference of International Federation of Automatic

Control (IFAC), 2004.

[20] F. Olyslager, J. Fortuny, A. Franchois, G. Nesti, and A. J. Sieber, "Numerical and

measurement based study of the resonance frequency technique to detect anti-personnel

mines," Ultra-Wideband, Short-pulse Electromagnetics 4. Edited by Heyman et al.,

Kluwer Academic/Plenum Publishers, New York, pp. 417-424, 1999.

[21] B. Heincke, J. V. D. Kruk, A. G. Green, and H. Horstmeyer, "Processing strategy for 3-D

georadar data acquired in areas characterized by rugged topography," In the Proceedings

of Tenth International Conference on Ground Penetrating Radar, pp. 1-4, 2004.

[22] Z. Anxing, J. Yansheng, and W. Wenbing, "Signal-to-noise Ratio Enhancement in

Multichannel GPR Data via the Karhunen-Loeve Transform," In the proceedings of

Progress In Electromagnetic Research Symposium, pp. 1-4, 2005.

[23] T. G. Savelyev, L. V. Kempen, and H. Sahli, "GPR anti-personnel mine detection:

improved deconvolution and time frequency feature extraction," In the Proceedings of

the SPIE- Detection and Remediation Technologies for Mines and Minelike Targets, vol.

5046, pp. 232-241, 2003.

[24] J. Zhang and B. Nath, "Image processing techniques of landmines: A Review," In the

proceedings of the international Conference on Intelligent Sensing and Information

Processing (ICISIP 2004), pp. 143-148, 2004.

[25] G. N. Crisp and A. J. Hill, "Humanitarian multi-sensor hand-held mine detector: design

of a GPR array," In the Proceedings of the SPIE- Detection and Remediation

Technologies for Mines and Minelike Targets VII, vol. 4742, pp. 1029-1037, 2002.

[26] L. Capineiri, P. Falorni, and C. Windsor, "Buried mine classification from three-

dimensional radar data," Insight, vol. 44(12), pp. 759-764, 2002.

[27] L. V. Kempen, A. Katartzis, V. Pizurica, J. Cornelis, and H. Sahli, "Digital Signal/Image

processing for Mine detection. Part 2: Ground based approach," In the proceedings of

Euro Conference on Sensor Systems and Signal Processing Techniques applied to the

Detection of Mines and Unexploded Ordnance (MINE'99), pp. 54-59, 1999.

27

[28] L. V. Kempen, E. Nyssen, H. Sahli, and J. Cornelis, "Pattern recognition experiments for

ultra-sonic and radar AP-Mine detection," Sustainable Humanitarian Demining: Trends,

Techniques and Technologies, pp. 297-304, 1999.

[29] G. Alli, C. Bonopera, A. Sarri, G. Pinelli, and G. D. Pasquale, "Data Processing for mine-

detection Polarimetric Ground Penetrating Radar array," In the proceedings of Tenth

International Conference on Ground Penetrating Radar, The Netherlands, pp. 1-4, 2004.

[30] F. Cremer, T. T. Nguyen, L. Yang, and H. Sahli, "Stand-off Thermal IR Minefield

Survey: System concept and experimental results," Proceedings of SPIE, vol. 5794, pp.

209 - 220, 2005.

[31] F. Cremer, W. D. Jong, and K. Schutte, "Infrared Polarization measurements and

modeling applied to surface-laid antipersonnel landmines," Optical Engineering, vol.

41(5), pp. 1021-1032, 2002.

[32] F. Cremer, W. D. Jong, and K. Schutte, "Infrared polarization measurements of surface

and buried anti-personnel landmines," In the proceedings of SPIE- Detection and

Remediation Technologies for Mines and Minelike targets VI, vol. 4394, pp. 164-175,

2001.

[33] T. T. Nguyen, D. N. Hao, P. Lopez, F. Cremer, and H. Sahli, "Thermal Infrared

Identification of Buried Landmines," In the proceedings of SPIE- Detection and

Remediation Technologies for Mines and Minelike targets X, vol. 5794, pp. 1-12, 2005.

[34] S. Sjokvist, M. Lundberg, I. Y. H. Gu, and M. Ulvklo, "Aspects of an Airborne System

for detection of Landmines using multispectral Imaging," In Proceedings of SSAB

(Swedish Society for Automated

Image Analysis) symposium on image analysis. Göteborg, March 9-10, 1999

http://db.s2.chalmers.se/download/publications/sjokvist_487.pdf (last accessed on

19/Jan/06).

[35] M. M. Eisl and M. Khalili, "ARC- Airborne Minefield Area Reduction," Final report of

Eudem Project, 2001-2003

http://www.arc.vub.ac.be/docs/Eudem2Scot2003_p265_ARC_1.0.pdf (last accessed on

15/Aug/05).

[36] V. Pizurica, A. Katartzis, J. Cornelis, and H. Sahli, "What can be expected from

Computerized Image Analysis Techniques for Airborne Minefield detection?," In the

proceedings (CDROM) of 2nd International Symposium on Operationalization of Remote

Sensing, Enschede, The Netherlands, 1999.

[37] F. Cremer, W. D. Jong, and K. Schutte, "Processing of polarimetric infrared images for

landmine detection," In the proceedings of 2nd International workshop on advanced

ground penetrating radar (IWAGPR), pp. 1-6, 2003.

[38] F. Cremer, W. D. Jong, K. Schutte, W. Liao, and B. A. Baertlin, "Detectibility of

Surface-laid Landmines with polarimetric IR sensor," In the proceedings of SPIE-

Detection and Remediation Technologies for Mines and Minelike targets VIII, vol. 5089,

pp. 1-12, 2003.

[39] F. Cremer, W. D. Jong, K. Schutte, J. T. Johnson, and B. A. Baertlein, "Surface mine

signature modelling for passive polarimetric IR," In the proceedings of SPIE- Detection

and Remediation Technologies for Mines and Minelike targets VII, vol. 4742, pp. 51-62,

2002.

[40] R. L. V. Dam, B. Boerchers, J. M. H. Hendrickx, and S. Hong, "Controlled field

experiments of wind effects on thermal signatures of buried and surface-laid landmines,"

28

In the Proceedings of SPIE- Defense and security symposium, 2004

http://infohost.nmt.edu/~rvd/pdf/spie2004_thermal.pdf (last accessed on 20/Sep/05).

[41] O. Gonzalez, A. Katartziz, H. Sahli, and J. Cornelis, "Pre-processing of polarimetric IR

images for landmine detection," In the proceedings of 22nd Symposium on Information

and Communication Theory, pp. 39-45, 2001.

[42] F. Cremer, J. G. M. Schavemaker, W. D. Jong, and K. Schutte, "Comparison of Vehicle-

mounted forward looking polarimetric infrared and downward-looking infrared sensors

for landmine detection," In Proceedings of SPIE- Detection and Remediation

Technologies for Mines and Minelike Targets VIII, vol. 5089, pp. 517-526, 2003.

[43] M. Schachne, L. v. Kempen, D. Milojevic, H. Sahli, P. v. Ham, M. Acheroy, and J.

Cornelis, "Mine detection by means of dynamic thermography: simulation and

experiments," In the Proceedings of Detection of abandoned landmines, vol. 458, pp.

124-128, 1998.

[44] C. T. Coutsomitros, A. Kokonozi, F. Andritsos, I. Vakalis, and L. v. Wijk, "Target

identification in Humanitarian demining using weak activation IR methods," In the

Proceedings of Euroconference- "Mine 99", Florence, Italy, pp. 131-136, 1999.

[45] M. Lundberg and I. Y. H. Gu, "A 3-D Matched Filter for Detection of Land Mines using

Spatio-Temporal Thermal Modeling," In the proceedings SPIE- Detection Technologies

for Mines and Minelike targets, vol. 2496, pp. 179-188, 2000.

[46] M. Lundberg, "Infrared Land Mine Detection by Parametric Modeling," In the

proceedings of IEEE International Conference on Acoustics Speech and Signal

Processing (ICASSP), 2001

http://db.s2.chalmers.se/download/publications/lundberg_849.pdf (last accessed on

17/Aug/05).

[47] L. Svensson and M. Lundberg, "Dual-band landmine detection using a Bayesian

approach," In the Proceedings of IEEE International Conference on Acoustics speech

and signal processing, 2002

http://db.s2.chalmers.se/download/publications/svensson_1093.pdf (last accessed on

05/Jun/2005).

[48] W. A. C. M. Messelink, K. Schutte, A. M. Vossepoel, F. Cremer, J. G. M. Schavemaker,

and E. d. Breejen, "Feature-based detection of landmines in infrared images," In the

Proceedings of SPIE- detection and remediation technology for mine and minelike

targets VII, vol. 4742, pp. 1-12, 2002.

[49] P. Lopez, H. Sahli, and D. Cabello, "Detection and Classification of Landmines from

Infrared Images," Removal and Neutralization of Landmines and UXO, 2003

www.clearfast.vub.ac.be/publications_ files/Lopez_Eudem2Scot_2003.pdf (last accessed on

07/Jul/05).

[50] S. Tjuatja, J. W. Bredow, A. K. Fung, and O. R. Mitchell, "Radar Imaging of Buried

Mine-Shaped Objects," In the proceedings of Geoscience and Remote Sensing

Symposium, pp. 2391-2393, 2000.

[51] P. Pregowski, W. Swiderski, R. T. Walczak, and K. Lamorski, "Buried mine and soil

temperature prediction by numerical model," In the proceedings of 15th World

Conference on Non-Destructive Testing, pp. 1392-1403, 2000.

[52] Y. Lostanlen, B. Uguen, G. Chassay, and H. D. Griffiths, "Modelling of Air-soil Interface

Clutter for Ultrawideband Radar," In the Proceedings of IEEE conference- Radar2000,

29

Washington, 2000 http://yves.lostanlen.free.fr/cv/pdf/radar00.pdf (last accessed on

21/May/05).

[53] M. Lundberg, "Reduction of Surface Clutter in Infrared Images with Visual Wavelength

Measurements," In the Proceedings of the IEEE 2000 International Geoscience and

Remote Sensing Symposium (IGARSS'00), pp. 2377-2379, 2000.

[54] T. Shi, G. O. Sauermann, and C. M. Rappaport, "Dual-frequency micro-wave-enhanced

infrared thermography," Subsurface Sensing Technologies and Applications, pp. 379-386,

2002.

[55] T. W. Miller, J. M. H. Hendrickx, and B. Boerchers, "Radar Detection of Buried

Landmines in Field soils," Vadose Zone Journal, vol. 3, pp. 1116-1127, 2004.

[56] R. L. V. Dam, B. Borchers, J. M. H. Hendrickx, and R. S. Harmon, "Effects of soil water

content and texture on radar and infrared landmine sensors: implications for sensor

fusion," In the proceedings of European Demining, 2003

http://www.ees.nmt.edu/Hydro/landmine/pub/eudem2003.pdf (last accessed on

19/Jan/06).

[57] J. G. M. Schavemaker, E. D. Breejen, F. Cremer, K. Schutte, and K. W. Benoist, "Depth

fusion for anti-personnel landmine detection," In the proceedings of SPIE- Detection and

Remediation Technologies for Mines and Minelike targets VI, vol. 4394, pp. 1071-1081,

2001.

[58] R. L. V. Dam, B. Boerchers, J. M. H. Hendrickx, and S. Hong, "Soil effects on thermal

signatures of buried non metallic landmines," In the Proceedings of SPIE- Detection and

Remediation Technologies for Mines and Minelike Targets VIII, vol. 5089, pp. 1210-

1218, 2003.

[59] C. Bruschini and B. Gros, "A survey of Current Sensor Technology Research for the

detection of landmines," In the proceedings of International workshop on Sustainable

Humanitarian Demining http://diwww.epfl.ch/w3lami/detec/susdemsurvey.html (last

accessed on 21/May/05), 1997.

[60] H. T. Haskett and J. T. Broach, "Automatic Mine detection Algorithm using Ground

penetration Radar Signatures," In the Proceedings of the SPIE- Detection and

Remediation Technologies for Mines and Minelike Targets IV, vol. 3710, pp. 942-952,

1999.

[61] B. Gros and C. Bruschini, "Sensor Technologies for the detection of antipersonnel mines

A survey of current research and system development," In the Proceedings of the

International Symposium on Measurement and Control in Robotics (ISMCR'96), 1996

http://diwww.epfl.ch/w3lami/detec/artismcr96.html (last accessed on 19/Jan/06).

[62] F. Cremer, E. D. Breejen, and K. Schutte, "Sensor Data Fusion for Anti-personnel Land-

mine Detection," In the proceedings of EuroFusion98, pp. 55-60, 1998.

[63] A. H. Gunatilaka, "Application of Multisensor Fusion to Improve Landmine Detection,"

Ph.D Thesis, The Ohio State University, 2000.

[64] F. Cremer, W. D. Jong, and K. Schutte, "Fusion of polarimetric infrared features and

GPR features of landmine detection," In the proceedings of 2nd International workshop

on advanced Ground Penetrating Radar (IWAGPR), pp. 1-6, 2003.

[65] F. Cremer, J. Schavemaker, E. D. Breejen, and K. Schutte, "Detection of anti-personnel

land-mines using sensor-fusion techniques," In the proceedings of EuroFusion99, pp.

159-166, 1999.

30

[66] E. D. Breejen, K. Schutte, and F. Cremer, "Sensor fusion for anti personnel landmine

detection: a case study," In the proceedings of SPIE- Detection and Remediation

Technologies for Mines and Minelike targets IV, vol. 3710, pp. 1235-1245, 1999.

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s:Bhuiyan, Alauddin;Nath, Baikunth

Title:Anti-personnel landmine detection based on GPR and IR imaging: a review

Date:2006-04

Citation:Bhuiyan, Alauddin, &Nath, Baikunth. (2006). Anti-personnel landmine detection basedon GPR and IR imaging: a review, Technical Report, Computer Science and SoftwareEngineering, The University of Melbourne.

Publication Status:Unpublished

Persistent Link:http://hdl.handle.net/11343/34827