Refraction and Dispersion Effects Compensation for ... - IEEE Xplore

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 12, DECEMBER 2007

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Refraction and Dispersion Effects Compensation for UWB SAR Subsurface Object Imaging Tian Jin, Student Member, IEEE, and Zhimin Zhou

Abstract—It is a trend to use airborne or vehicle-borne ultrawideband synthetic aperture radar (UWB SAR) to quickly detect subsurface objects, i.e., landmines or minefields, over large areas from a safe standoff distance. The traditional image formations without considering the refraction and dispersion of electromagnetic wave will lead to locating error and defocusing for subsurface objects. In this paper, an efficient refraction and dispersion effects compensation method is proposed, which can be integrated with the traditional three-stage automatic target detection framework for detection of subsurface landmines. The compensation method can focus and correctly locate multiple objects buried in different soils with different depths over large areas, which satisfies the operating requirement of an airborne or vehicle-borne UWB SAR system. The proposed method is proved efficient for subsurface object imaging by the field data collected by a UWB SAR system. Index Terms—Refraction and dispersion, subsurface imaging, synthetic aperture radar (SAR), ultrawideband (UWB).

I. I NTRODUCTION

L

ANDMINES are causing enormous humanitarian and economic problems in many countries all over the world. Experts estimate that up to 110 million landmines need to be cleared, and more than 500 civilians are killed or maimed every week by landmines [1]. The detection and remediation of landmines have become important topics because of humanitarian and environmental impacts. Many sensors have been developed to address this problem, including electromagnetic induction, magnetometers, and down-looking ground-penetrating radar (GPR). Although such sensors are often effective in detecting and discriminating landmines, they generally require that the sensor be close to the ground. At the current clearance rate, it will take about 1100 years to remove all existing landmines [2]. In such situation, wide-area surveillance is often required. Airborne or vehicle-borne ultrawideband synthetic aperture radar (UWB SAR), working in side-looking or forward-looking mode, has the ground-penetrating capability to detect subsurface objects [3], [4], i.e., landmines or minefields. The imaging geometry of the side-looking or forward-looking UWB SAR is different from that of the down-looking GPR. Although this down-looking type of system shows a very promising detection capability, its main drawback is that it is time consuming to use this type of system for large-area interrogation, and short standoff distance is a problem as well. Manuscript received February 14, 2007; revised May 30, 2007. The authors are with the College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2007.905105

The classical automatic target detection (ATD) for UWB SAR is composed of three stages [5]: 1) image formation; 2) prescreening; and 3) discrimination. The typical UWB SAR image formations are backprojection (BP) [6] and its fast version [7], ω−k [8], and so on. After image formation, the prescreening stage slides a computationally efficient feature mask over the image to locate regions of interest (ROIs). Typical prescreening features strictly distinguish object regions from background on the basis of brightness. The two-parameter constant false alarm rate (CFAR) is a popular baseline prescreening feature [9]. Each ROI extracted from the entire SAR image contains a minelike object. The following discrimination stage is to classify those minelike objects into mines and nonmines, which are also called target and clutter in landmine discrimination, respectively. Traditional UWB SAR image formations are usually on the assumption of a homogeneous propagation medium, which take no consideration of the electromagnetic wave refraction and dispersion in ground-penetrating applications. Some modified BP [10]–[12] or ω−k [13], [14] algorithms have been proposed for subsurface object imaging. These subsurface image formations deal with the refraction and dispersion phenomena in imaging processing on raw echo data. The modified BP algorithms calculate the time delay in air and soil, respectively, which determine the refraction point by solving a fourth-order polynomial equation. However, such an equation has to be repeatedly solved for each point in an image scenario with SAR at each aperture position, which leads to considerable computation efforts. The modified ω−k algorithm proposed a new version of the Stolt transform with the refraction and dispersion effects compensation (RDEC). However, the modified ω−k algorithm is hard to combine with motion compensation, which is the common drawback of all frequency-domain image formations. The three-stage ATD framework for subsurface landmine detection can adopt subsurface image formations in the imaging stage, as depicted in Fig. 1. Unfortunately, all aforementioned subsurface imaging algorithms can only deal with objects buried in the same soil with the same depth, which is too ideal for practical situations. Because only one depth and one soil permittivity can be used in either determining the refraction point or modifying the Stolt transform, it is hard to deal with the situation of a number of landmines buried in different soils with different depths. Therefore, some landmines are focused, whereas others are defocused in the entire SAR image, which will lead to wrong location for those ROIs containing defocused landmines and degrade the landmine detection performance for a low signal-to-noise ratio (SNR) as well. An alternative

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Fig. 3.

Photographs of the Rail-GPSAR system. TABLE I MAJOR PARAMETERS OF THE RAIL-GPSAR SYSTEM

Fig. 1. Traditional three-stage ATD framework for detection of subsurface landmines.

The remainder of this paper is organized as follows. In Section II, we give a brief description on the Rail-ground penetrating synthetic aperture radar (GPSAR) system. All experimental data in this paper are collected by the system. In Section III, we develop the echo model for a subsurface object, based on which the refraction and dispersion effects (RDEs) on a SAR image are quantitatively analyzed. In Section IV, the RDEC method is proposed. The effectiveness of the proposed method is demonstrated in Section V, which is based on the Rail-GPSAR measured data. Finally, we conclude in Section VI. II. R AIL -GPSAR S YSTEM D ESCRIPTIONS Fig. 2. Improved three-stage ATD framework for detection of subsurface landmines.

method is to perform the aforementioned subsurface image formation, i.e., the modified BP algorithm, several times with separate buried depth and soil parameters to focus each landmine, but this method is computationally infeasible for largearea interrogation. In this paper, we propose a novel RDEC method, which can deal with the situation of multiple objects buried in various soil environments with different depths. The proposed method is performed on selected subimages from the entire SAR image formed by traditional image formation, i.e., the BP algorithm, where the subimage selection can be accomplished by the prescreening stage to reduce computation load. Therefore, the proposed compensation method can be naturally embedded in the three-stage ATD framework, which is called improved ATD framework, as depicted in Fig. 2.

The data used in this paper are collected by a rail-borne impulse UWB SAR system named Rail-GPSAR. A photograph of the Rail-GPSAR system is shown in Fig. 3. The system consists of one transmit and one receive ridged transverse electromagnetic horn antenna, whose geometry center is 0.83 m away from each other. The quasi-monostatic configuration is adopted in the imaging processing with the assumed radar platform position being the center between the two antennas. An oscilloscope is used to record the received signal with a sample rate of 10 GHz. The sampled data by the oscilloscope are 8 bits, but considering the receiver noise factor, the effective number of bits is about 6.5. Other major parameters of the system are shown in Table I. The majority of the hardware is mounted on a trolley with a geodimeter to measure the position for motion compensation. The radar platform moves along the rail track to facilitate stripmap SAR processing to form a side-looking image of an area 200 m2 . In the imaging area, there is a 6 × 6 m pool with

JIN AND ZHOU: REFRACTION AND DISPERSION EFFECTS COMPENSATION

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εr = εr − jεr is a complex-valued function of frequency. According to the Snell law, the incident angle θi and the complex refraction angle θt have the following relationship: √ sin θi = εr sin θt

(1)

and the amplitudes of the propagation vectors in air k and in soil kt have the following relationship: kt =

Fig. 4.

UWB SAR 3-D imaging geometry.

Fig. 5. Geometry illustrating electromagnetic wave propagation in twolayered medium. ρ1 and ρ2 are the electromagnetic wave propagation path of the buried object, respectively. ρ1 is the true slant range of the buried object, and ρ4 is the estimated slant range by the traditional image formation, i.e., the BP algorithm, without considering the RDE.

1-m depth. The pool can be filled with sand or clay soil for the Rail-GPSAR system to collect data in different scenarios. The 3-D imaging geometry is depicted in Fig. 4, where r, x, y, and z are the slant range, ground range, azimuth, and height, respectively. III. S UBSURFACE O BJECT E CHO M ODEL AND RDE ON SAR I MAGE A. Subsurface Object Echo Model in Two-Layered Medium The radar platform moves with a constant velocity along the azimuth direction. At each aperture position (0, u, HR ), an ideal transmitting antenna emits a wide-bandwidth pulse signal p(t), where t and u are called fast time and slow time, respectively. Because the radar platform velocity is much slower than the electromagnetic wave speed in free space, the returns reflected by the illuminated terrain are recorded almost at the same transmitting position. Let us consider a shallow buried point scatterer at (xn , yn , zn ) with unit scattering amplitude. The electromagnetic wave propagation in the air–soil two-layered medium is depicted in Fig. 5. Denote soil relative permittivity and relative permeability as εr and µr , respectively, where µr ≈ 1 and

√

εr k.

(2)

The propagation vector k is also called a fast-time wavenumber in SAR processing with k = 2πf /c, where f and c are the electromagnetic wave frequency and speed in air, respectively. Because εr is complex, θt is also complex, which makes the propagation vector in soil, kt = β − jα is a complex vector with α and β being the normal vectors of the constant amplitude planes and the constant phase planes, respectively. The propagation direction in soil is determined by β; the angle between β and the normal direction of ground is ϕt , which is called the real refraction angle, as depicted in Fig. 5. The echo phase history of the point scatterer will be defined by the electrical length of the two-way path traveled by a spherical wave from the antenna to the location of the point scatterer. Considering the relationship of the propagation vectors in air and soil, as shown in (2), the total electrical lengths in air and soil in Fig. 5 can be expressed as the equivalent electrical length ρ in air as ρ = ai · ρ1 +

√ εr at · ρ2

(3)

where · is the dot product operator, and ai and at are the unit vectors of k and kt , respectively. Because the angle between k and ρ1 is zero and the angle between k and ρ2 is |ϕt − θt |, we have ai · ρ1 = ρ1 at · ρ2 = ρ2 cos (|ϕt − θt |) zn (cos ϕt cos θt + sin ϕt sin θt ) = cos ϕt

(4)

(5)

where ρ1 and ρ2 are the amplitudes of ρ1 and ρ2 , respectively. Plugging (4) and (5) into (3), and considering (1), we have √ ρ = ρ1 + ∆ρ + zn cos θt εr

(6)

∆ρ ≈ zn tan ϕt sin θi .

(7)

with

For an airborne or vehicle-borne UWB SAR system, its detection range is usually hundreds or tens of meters, which is much larger than the buried depth. Therefore, based on the electromagnetic wave propagation geometry in a two-layered medium, (6) can be approximately expressed as √ ρ = ρ3 + zn cos θt εr .

(8)

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Furthermore, taking the relationship of θi and θt as (1) into account, (8) can be rewritten as ρ = ρ3 + zn εr − sin2 θi . (9) Because εr is a function of frequency, we quantify the RDE in the fast-time wavenumber domain. Therefore, according to the electromagnetic wave propagation model and the equivalent electrical length of (9), the echo of the subsurface point scatterer can be expressed as the 2-D function of t and u in the form of convolution with respect to t as 1 p(t) ⊗t FT−1 k→t ρ23 × exp −j2k ρ3 +zn εr −sin2 θi Ti (θi )T2 (θi )

s(t, u) ≈

with

ρ3 =

2 x2n + (yn − u)2 + HR

θi ≈ arccos(HR /ρ3 )

(10)

   T2 (θi )=

 

2

√ √

εr cos θi + 2

√

εr −sin2 θi

εr −sin2 θi

cos θi +

√

εr −sin2 θi

,

,

4δy Θ = 2 arcsin Kh λ c

(14) for H-polarization.

According to (12), θi varies when the radar platform moves along the azimuth direction, which can be also expressed by the aspect angle θa and the broadside incident angle θi0 as

where θa and θi0 can be approximately calculated via

u − yn θa ≈ arctan 2 x2n + HR θi0 ≈ arctan(xn /HR )

where δ(·) is the Dirac impulse function, and h((u − y)/r) is the window to specify the aperture length and shape. The average weighted window is u−y 1, for |(u − y)/r| ≤ tan(Θ/2) h = (19) 0, otherwise r

(12)

for V-polarization

θi = arccos(cos θa cos θi0 )

(18)

where Θ is the integration angle determined by the desired azimuth resolution δy as

εr −sin θi

ε2r −εr sin2 θi

2 2 r + (u − y)2 dudt ×δ t− c

(11)

where ⊗t is the 1-D convolution operator with respect to t, FT−1 k→t (·) is the 1-D inverse Fourier transform (FT) operator with respect to t, P (k) is the spectrum of the transmitted pulse signal p(t), and T1 (θi ) and T2 (θi ), as shown in the following equations, are the transmission factors from air to soil and back to air, which are determined by the polarization modes of the transmit and receive antennas, respectively: √  2 εr cos θi √  , for V-polarization + εr −sin2 θi (13) T1 (θi )= εr cos θ2icos  √ θi , for H-polarization 2 cos θi +

easy to integrate with motion compensation and has a fast version to reduce its computational complexity, so we use the BP algorithm to analyze the RDE on SAR image. The BP algorithm with a constant integration angle, which is referred to as constant integration angle backprojector (CIAB), is [15] t2 r−1 h (r, u − y) s(t, u) fCIAB (r, y) =

(15)

(20)

with λc being the wavelength of the center frequency, and Kh = 0.89 for the average weighted window. For a subsurface object, its image formed by the CIAB algorithm can be approximately rewritten as fCIAB (r, y) ≈ fPSF (r, y) ⊗r,y fRDE (r, y)

(21)

where ⊗r,y is the 2-D convolution operator with respect to r and y, fPSF (r, y) is the point spread function (PSF) of the CIAB algorithm for an ideal surface point scatterer, and fRDE (r, y) is to quantify the RDE on the subsurface object, which is referred to as the RDE factor. Based on the subsurface object echo of (10) and the relationship between the k − θa domain and the kr − ky domain of the CIAB algorithm [15], the following can be derived: k = 0.5 kr2 + ky2 (22) θa = arctan(−ky /kr ) with kr and ky being slant-range wavenumber and azimuth wavenumber, respectively, the RDE factor can be expressed as

(16) (17)

fRDE (r, y) = FT−1 kr ,ky →r,y 2 2 2 kr + ky εr − sin θi T1 (θi )T2 (θi ) × exp −jzn

with the condition of HR zn . B. Quantitative Analysis of RDE on SAR Image Because SAR has only the 2-D imaging capability, the 3-D location of the object scattering is mapped into the 2-D imaging plane r−y, which is also called the slant plane. Among traditional UWB SAR image formations, the BP algorithm is

(23) where FT−1 kr ,ky →r,y [·] is the 2-D inverse FT operator with respect to r and y. To analyze the RDE on locating and focusing, we further divide the RDE factor in (23) into two terms as fRDE (r, y) ≈ fRDE1 (r) ⊗r fRDE2 (r, y)

(24)


with

fRDE1 (r) = δ r−zn εr,∞ −sin2 θi0

(25)

fRDE2 (r, y) = FT−1 kr ,ky →r,y

ky2 (εr −1) × exp −jzn T1 (θi )T2 (θi ) 2kr εr −sin2 θi0 (26)

where ⊗r is the 1-D convolution operator with respect to r, and εr,∞ is the real part of εr at high frequency. The first term fRDE1 (·) quantifies the locating error in the r direction by the CIAB algorithm, and the second term fRDE2 (·) indicates that refraction and dispersion will cause defocusing in both the r and y directions. IV. RDEC IN THE I MAGE D OMAIN A. RDEC on Selected Subimages All the subsurface image formations in the previous literature compensate the RDE on the raw echo. The antenna-received data are the echo of all scatterers in the imaging area, which may be buried in different soils with different depths. However, their echo is hard to be separated in the echo domain, which means that these subsurface image formations cannot compensate the RDE of all subsurface objects at the same time. Furthermore, their compensation schemes need the prior information of buried depths, which is too strict for practical applications. UWB SAR can obtain a high-resolution image of an area of interest, where different scatterers are separated and their subimages can be segmented from the entire SAR image. Therefore, a practical approach is to separately compensate the RDE on selected subimages according to their buried depths and soil environments. Denote the subimage containing a subsurface object at (xn , yn , −zn ) as f (r, y), which is segmented from the entire image fCIAB (r, y). The RDE are quantitatively expressed by the RDE factor as (23). Therefore, the RDE can be removed by compensating the phase term of the RDE factor as f˜(r, y) = FT−1 kr ,ky →r,y FTr,y→kr ,ky [f (r, y)] FRDEC (kr , ky ) (27) with FRDEC (kr , ky ) 2 ˜ 2 2 kr + ky ε˜r − sin θi exp jd Re T1∗ (θ˜i )T2∗ (θ˜i ) = T1 (θ˜i )T2 (θ˜i ) (28) where FRDEC (·) is called the RDEC factor to eliminate the phase aberration caused by the RDE, FTr,y→kr ,ky [·] is the 2-D FT operator with respect to r and y, Re(·) is the real part

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operator, ε˜r and θ˜i are estimations of εr and θi , respectively, and d is the compensation depth. According to (15) and (22), θ˜i can be calculated as   kr (29) θ˜i = arccos  cos θ˜i0  2 kr + ky2 where θ˜i0 is the estimation of θi0 . Because |FRDEC (·)| ≡ 1, the electromagnetic wave attenuation caused by soil is not compensated, which guarantees that only when d is equal to the true buried depth zn will the maximum envelope amplitude of f˜(r, y) be obtained. In practical applications, the three parameters θ˜i0 , ε˜r , and d in FRDEC (·) are unknown. θ˜i0 can be computed as θ˜i0 ≈ arccos(HR /rnc )

(30)

where rnc is the slant-range coordinate of the subimage center. According to (25), for the subsurface object, the error between rnc and the true slant-range location is about

zn εr,∞ − sin2 θi0 , which is much smaller than rnc for an airborne or vehicle-borne system. Therefore, the estimation error of (30) can be neglected. The estimation of the soil relative permittivity is another key parameter. In previous literature, the soil dielectric parameter estimation methods can be grouped in four categories: phenomenological models, volumetric models, empirical or semiempirical model, and effective medium models [16]. Therefore, ε˜r can be obtained using these methods. In Rail-GPSAR system data processing, the semiempirical model proposed by Peplinski [17], [18] is adopted. The predicted relative permittivity of clay soil (clay fraction is 0.7) with 10% water content in volume is compared with the measured result by the Agilent 85070E dielectric probe kit and its associated 85071E materials measurement software. The predicted and measured results are depicted in Fig. 6, which shows that the predicted results fulfill the precision requirement for RDEC. The determination of the last parameter d is the most difficult but the most important. As discussed above, the more the similarity between d and zn , the larger the envelop amplitude of f˜(r, y). Therefore, d can be estimated via d = arg max {Mean [f (r, y; di )]}

(31)

di

where di are candidate values of d, f (r, y; di ) is the amplitude image of f˜(r, y) compensated with di , and Mean[·] is the average operator. The value of d can also be considered as the estimation of buried depth of the object. For metallic antitank landmines, the typical buried depth is 0.1 m. Therefore, di is from 0 to 0.3 m with a 0.05-m interval in Rail-GPSAR system data processing. In this case, the maximum error between d and zn is 0.025 m; its associated maximum slant-range locating error is about 0.15 m even with εr,∞ = 36. For most soils, εr,∞ is usually less than 36. After determining the appropriate value of d, the defocused subimage can be refocused and the locating error can be corrected.

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Fig. 7.

Bipolar image formed by the BP algorithm with extracted ROIs.

will degrade their SNR. Therefore, a lower CFAR threshold should be adopted in the improved ATD framework, which will produce more false alarms as well. Among these false alarms, most of them are surface scatterers since these surface scatterers are well focused by traditional image formations, i.e., the BP algorithm, and have relatively higher SNR than that of defocused subsurface objects. However, after RDEC, the SNR of those subsurface objects will increase, and thus, a higher CFAR threshold can be used to reduce the false alarm rate while maintaining a high detection probability of both surface and subsurface landmines in the prescreening stage. Because the compensation on selected subimages can be accomplished by 2-D fast Fourier transform, the computational load will not largely increase. V. R AIL -GPSAR S YSTEM R EAL D ATA R ESULTS

Fig. 6. Comparison of the predicted and measured relative permittivity of clay soil with 10% water content in volume. (a) Real part. (b) Imaginary part.

B. Discussions on Subimage Selection Our proposed compensation scheme is actually composed of two steps: subimage selection and subimage compensation. The second compensation step has been thoroughly discussed above. Although the subimage selection step is combined with the prescreening stage to improve the efficiency of the whole processing procedure, the criterion of the subimage selection is slightly different from that of the traditional prescreening processing. The prescreening stage extracts some ROIs from the entire SAR image to reduce the computation load of the following discrimination stage. The two-parameter CFAR technique is on brightness feature to extract some pixels of interest (POIs). Since one object can produce multiple POIs, the POIs in object-size regions are clustered together. Then, a region with certain size around each cluster center is extracted as the ROI. For subsurface landmines, defocusing caused by RDE

First, consider the scenario with five landmines. The imaging result of the BP algorithm is depicted in Fig. 7, where 12 ROIs are extracted by the CFAR technique. Among these 12 minelike objects, there are five ROIs containing landmines and seven ROIs containing clutter, which are marked with M and C, respectively. The five ROIs that contain landmines are marked as M1 to M5, and the seven ROIs that contain clutter are marked as C1 to C7 in Fig. 7. The soil in the image area is clay soil but with different water contents. The water content in volume of the half soil near the rail is about 10% (from 5 to 8.8 m in slant range), and the rest is about 15% (from 8.8 to 11 m in slant range). M1, M2, and M3 are buried about 0.15 m in the soil with 10% water content, whereas M4 and M5 are buried about 0.1 m in the soil with 15% water content. The true slant-plane coordinates of the five landmines and their estimated coordinates with the centers of the extracted ROIs are shown in Table II. The average locating error in azimuth is 0.016 m, which can be neglected, but the average locating error in slant range is 0.45 m, which completely gives the wrong position. RDEC is performed on the subimages of the extracted ROIs. Take the second landmine marked as M2, for example. M2


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TABLE II TRUE LOCATION AND ESTIMATED LOCATION OF THE FIVE LANDMINES WITHOUT RDEC

TABLE III AVERAGE PIXEL VALUES OF ENVELOPE AMPLITUDE SUBIMAGES WITH DIFFERENT COMPENSATION DEPTHS

TABLE IV SPATIAL RESOLUTION COMPARISON AND FINAL ESTIMATED LOCATION OF FIVE LANDMINES WITH RDE COMPENSATED

is buried 0.15 m in the sand clay with 10% water content. The estimated broadside incident angle θ˜i0 is 62.6◦ with its cosine value being 0.46, whereas the true broadside incident angle θi0 is 60.6◦ with its cosine value being 0.49. Using θ˜i0 instead of θi0 will cause little error in compensation. ε˜r can be obtained by the aforementioned semiempirical model, as shown in Fig. 6. Several values of d are used to compensate the RDE on the extracted subimage of M2. The average pixel values of envelope amplitude subimages with different compensation depths are shown in Table III. According to the criterion in (31), the optimal value of d is 0.15 m, which is the nearest to the true buried depth. The remaining subimages of the ROI are all processed in the same way. The slant-range resolution δr and azimuth resolution δy of the five landmines and their final obtained locations are shown in Table IV, where the spatial resolution is defined by the 3-dB width of the mainlobe in slant range and azimuth directions, respectively. The maximum locating errors in slant range and azimuth are less than 0.02 and 0.01 m, respectively, which fulfill practical requirements. The residual locating error is mainly caused by the approximation of their broadside incident angles and the prediction error of soil relative permittivity. After RDEC, the SNR of each landmine also increases. However, most clutter ROIs contain surface scatterers, and thus, their SNR will not increase. According to the proposed improved ATD framework, after RDEC, a higher CFAR threshold is used to choose less ROIs again. For the 12 ROIs in Fig. 7, there are a total of nine ROIs finally chosen, including the five landmine ROIs, and only four clutter ROIs, where the false alarm rate greatly decreases.

Fig. 8. ROC curves of the prescreening results of the traditional ATD framework and the improved ATD framework with the proposed compensation method.

To verify the efficiency of the proposed method in improving the prescreening performance, more real data are used. There are 40 landmines buried at 5, 10, 15, 20, and 25 cm in clay and sand soils. The receiver operating characteristic (ROC) curves of the prescreening results of the traditional ATD framework and the improved ATD framework with the proposed compensation method are depicted in Fig. 8. The ROC curves show that the proposed compensation method can significantly improve the prescreening performance.

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VI. D ISCUSSIONS AND C ONCLUSION In this paper, a novel RDEC method is proposed for subsurface object imaging. The method compensates the RDE in the image domain, which can be integrated with the traditional three-stage ATD framework to improve the processing efficiency of UWB SAR landmine detection over large areas. The RDEC method and its associated improved ATD framework have been verified by the field data collected by the RailGPSAR system. Airborne or vehicle-borne UWB SAR affords a promising technique for wide-area detection of landmines. However, further research need to be carried out in the following three aspects: soil parameter estimation, geometric correction, and landmine discrimination. The semiempirical model proposed by Peplinski [17], [18] is used in this paper to estimate the soil parameters, which needs a priori knowledge of the soil conditions, i.e., water content. To perform wide-area detection of landmines, a more efficient soil parameter estimation method is required. Because SAR has only 2-D imaging capability, the imaging problem is usually discussed in the imaging plane (also called slant plane). In landmine detection, the ground-plane coordinates of subsurface objects are needed, and thus, the geometric correction processing is required to map the formed image from the slant plane onto the ground plane. The assumption of plat ground plane is too ideal in practice. Therefore, the digital elevation model of the imaged scene is needed in the geometric correction processing to deal with the practical situations [19]. Landmine discrimination in the ATD framework is not presented in this paper, whose task is to eliminate clutter in extracted ROIs to reduce the false alarm rate of the final detection results further. Landmine discrimination is another challenge for landmine detection. The proposed RDEC method affords correct location information for final detected landmines. R EFERENCES [1] Y. Sun and J. Li, “Time-frequency analysis for plastic landmine detection via forward-looking ground penetrating radar,” Proc. Inst. Electr. Eng.—Radar, Sonar Navig., vol. 150, no. 4, pp. 253–261, Aug. 2003. [2] P. Gao and L. M. Collins, “Two-dimensional generalized likelihood ratio test for land mine and small unexploded ordnance detection,” Signal Process., vol. 80, no. 8, pp. 1669–1686, Aug. 2000. [3] L. Carin, N. Geng, M. McClure, J. Sichina, and L. Nguyen, “Ultra-wideband synthetic-aperture radar for mine-field detection,” IEEE Antennas Propag. Mag., vol. 41, no. 1, pp. 18–33, Feb. 1999. [4] J. Kositsky, R. Cosgrove, C. Amazeen, and P. Milanfar, “Results from a forward-looking GPR mine detection system,” Proc. SPIE, vol. 4742, pp. 206–217, Aug. 2002. [5] L. M. Kaplan, J. H. McClellan, and S.-M. Oh, “Prescreening during image formation for ultrawideband radar,” IEEE Trans. Aerosp. Electron. Syst., vol. 38, no. 1, pp. 74–88, Jan. 2002. [6] J. W. McCorkle, “Focusing of synthetic aperture ultra wideband data,” in Proc. Int. Geosci. and Remote Sens. Symp., Fairborn, OH, 1991, pp. 1–5. [7] L. M. H. Ulander, H. Hellsten, and G. Stenstrom, “Synthetic-aperture radar processing using fast factorized back-projection,” IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 3, pp. 760–776, Jul. 2003. [8] C. Cafforio, C. Prati, and F. Rocca, “SAR data focusing using seismic migration techniques,” IEEE Trans. Aerosp. Electron. Syst., vol. 27, no. 2, pp. 194–207, Mar. 1991. [9] L. M. Novak, S. D. Halversen, G. J. Owirka, and M. Hiett, “Effects of polarization and resolution on SAR ATR,” IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 102–116, Jan. 1997.

[10] J. Andrieu, F. Gallais, V. Mallepeyre, V. Bertrand, B. Beillard, B. Jecko et al., “Land mine detection with an ultra-wideband SAR system,” Proc. SPIE, vol. 4742, pp. 237–247, Aug. 2002. [11] J. Fortuny-Guasch, “A novel 3-D subsurface radar imaging technique,” IEEE Trans. Geosci. Remote Sens., vol. 40, no. 2, pp. 443–452, Feb. 2002. [12] J. Groenenboom and A. G. Yarovoy, “Data processing for landmine detection dedicated GPR,” Proc. SPIE, vol. 4084, pp. 867–871, Apr. 2000. [13] P. T. Gough and B. R. Hunt, “Synthetic aperture radar imaging reconstruction algorithms designed for subsurface imaging,” in Proc. Int. Geosci. and Remote Sens. Symp., Singapore, 1997, pp. 1588–1590. [14] J. Song, Q. H. Liu, P. Torrione, and L. Collins, “Two-dimensional and three-dimensional NUFFT migration method for landmine detection using ground-penetrating radar,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp. 1462–1469, Jun. 2006. [15] R. Rau and J. H. McClellan, “Analytic models and postprocessing techniques for UWB SAR,” IEEE Trans. Aerosp. Electron. Syst., vol. 36, no. 4, pp. 1058–1074, Oct. 2000. [16] R. L. Van Dam, B. Borchers, and J. M. H. Hendrickx, “Methods for prediction of soil dielectric properties: A review,” Proc. SPIE, vol. 5794, pp. 188–197, Jun. 2005. [17] N. R. Peplinski, F. T. Ulaby, and M. C. Dobson, “Dielectric properties of soils in the 0.3–1.3-GHz range,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 803–807, May 1995. [18] N. R. Peplinski, F. T. Ulaby, and M. C. Dobson, “Corrections to ‘Dielectric properties of soils in the 0.3–1.3-GHz range’,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 6, p. 1340, Nov. 1995. [19] J. C. Curlander and R. N. Mcdonough, Synthetic Aperture Radar: Systems and Signal Processing. New York: Wiley, 1991, ch. 8.

Tian Jin (S’07) was born in Wuhan, China, in 1980. He received the B.S. degree in information engineering and the M.E. and Ph.D. degrees in information and communications engineering from the National University of Defense Technology, Changsha, China, in 2002, 2003, and 2007, respectively. He is currently a Lecturer with the College of Electronics Science and Engineering, National University of Defense Technology. His research interests include ultrawideband synthetic aperture radar image formation and automatic target detection and recognition.

Zhimin Zhou received the B.S. degree in aeronautical radio measurement and control and the M.S. and Ph.D. degrees in information and communication engineering from the National University of Defense Technology, Changsha, China, in 1982, 1989, and 2002, respectively. Since 1998, he has been a Professor with the College of Electronics Science and Engineering, National University of Defense Technology, where he was a Teaching Assistant in 1983 and became a Lecturer and then an Associate Professor in 1988 and 1993, respectively. His current research interests include ultrawideband SAR system and real-time processing. Dr. Zhou is a Senior Member of the Chinese Institute of Electronics.