Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
1
Surface mine signature modeling for passive polarimetric IR Frank Cremerabc , Wim de Jonga , Klamer Schuttea , Joel T. Johnsond and Brian A. Baertleind a TNO
Physics and Electronics Laboratory, P.O. Box 96864, 2509 JG, The Hague, The Netherlands b Pattern Recognition Group, Delft University of Technology, Delft, The Netherlands c Section of Applied Geophysics, Delft University of Technology, Delft, The Netherlands d Ohio State University, ElectroScience Laboratory, Columbus, OH, USA ABSTRACT
A specular model has been used to predict the passive polarimetric infrared (IR) signature of surface-laid landmines. The signature depends on the temperature of the landmine and the sky radiance. The temperature of the landmine is measured using a thermocouple. The signature itself is measured using a polarimetric IR camera setup. The predictions are fit to the measurements using the refractive index as an optimisation parameter. The obtained refractive indices of each landmine type are consistent, but for the PMN landmine much lower than determined in a previous indoor experiment. Throughout the measurement day, the average landmine polarimetric signature was higher than the average background signature. Moreover the polarimetric signature appears to be a more robust indicator of the shape of the landmine’s top surface than the normal IR signature. A simulator of passive polarimetric imagery is also being developed. That work is based on a physical model for both the thermal and radiometric processes, and it includes a finite-element solution for the heat transfer problem, ray tracing to describe the incident sunlight and the effects of shadowing, and analytical models for the Mueller matrices of rough dielectric surfaces. Preliminary results from that model show substantial qualitative agreement with measured images. Keywords: infrared polarisation model, model validation, outdoor measurements, landmine detection
1. INTRODUCTION The thermal infrared camera is one sensor used for the detection of surface and flush buried landmines. Clutter is a limitation for the detection of landmines with IR cameras. It is known that, within the visual and IR bands, polarisation gives extra information about objects and their surfaces. With a (rotating) polarisation filter in front of a midwave infrared (MWIR) camera, polarimetric MWIR can be measured. The polarimetric IR signature of landmines differs from the cluttered background, because it has a different surface roughness. To predict the polarimetric IR signature for diverse conditions a validated model is needed. In previous work a specular model of the polarisation signature has been developed and validated in a controlled indoor experiment. This model has proven to be able to predict the polarisation signature sufficiently in this situation. For further validation, an outdoor experiment was performed. In this outdoor experiment, several types of landmines have been used among them the PMN landmine that was used in the previous experiment. The model predictions are compared to the polarimetric signature as measured by the polarimetric camera setup. As shown in this paper, the landmines show a pronounced polarimetric signature and this agrees reasonable well with the predictions. Moreover the polarimetric images seem to be better for automatic detection of surface-laid landmines than the normal MWIR images. To reach high detection rates for surface and buried landmines, this polarimetric camera must be integrated into a multi-sensor system. This paper will concentrate on the measured and predicted polarimetric signature of surface AP and AT landmines in a bare sand background. To better understand the phenomena that arise in polarimetric signatures, we are also developing an image simulator that builds on prior work in thermal modeling of buried mines. This effort permits us to explore in a unified, self-consistent manner, the effects of spatial variations in material types, surface properties, and time-dependent changes in temperature, incident radiation, and shadowing. Further author information: (Send correspondence to Frank Cremer) F.C.: E-mail:
[email protected], Telephone: +31 70 374 0795, Fax: +31 70 374 0654 B.A.B.: E-mail:
[email protected], Telephone: +1 614 292 0076
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
2
In the next two sections, Sections 2 and 3, the specular polarimetric model and the image simulator are briefly discussed. Then in Section 4 the measurements are described. These measurements are analysed and compared to the model predictions in Section 5. In this section the refractive index is determined for the different landmines. Finally in Section 6 the conclusions are given.
2. POLARIMETRIC IR MODELING
Previously, a simple polarimetric IR model was derived. In that model it is assumed that a single source ’illuminates’ on the landmine. This model has been validated indoors and has proven to be sufficient to describe the indoor measurements. The assumptions on which this model is based are: 1. The material of the landmine can be described by a single refractive index for the wavelength band used (MWIR). 2. The material of the landmine is opaque, meaning that there is no transmission of radiation through the landmine. 3. The surface of the landmine is specular for reflection and obeys the Kirchhoff law for radiation. 4. The temperature of the landmine is constant, since the landmine is assumed to be in thermal equilibrium with its surroundings. 5. The single source, that is ’illuminating’ the landmine is unpolarised. 6. The spectral sensitivity of the MWIR camera is constant throughout the wavelength band, ranging from 3 to 5 m. 7. The polarisation filter is ideal for the wavelength band. 8. The transmission through air is 100% and, thus, there is no path radiance. For our outdoor experiment, it is assumed again that there is only one source: the sky. Only linear polarisation is considered for this model; circular polarisation is not modelled and not measured. Then according to this model, the Stokes parameters , and for a flat horizontal surface are given by :
!#"
$ )*,+ %'& (
(.-
!#"
)*5 $ % &( ' =>
(.-
#"
)//0 )//0
121! )34657 121! )8 9/0 +: 121; ; &
121! )34657 121! )8 9/0 polarisation filter is given for each pixel by: {z{
w
xvy>|"} 1,1 s v+~
u
~
xvy>65 w w
xvy
xvy>65
with ~ w the measured value of the scene (in bits), ~ the blackbody with low and high temperature respectively.
s
s ~
s ~
xvy> w
x,y> w w
x,y> & u
and ~
w
121 u 657 1,1 s 0 xvy
(4)
the measured average calibration value of
First, all images were calibrated using the calibration closest (in time) to the measurements. Although this produces good results for a single measurement set, it suffers from continuity problems. Due to switching from one calibration set to the other, discontinuities occured in the measured radiance. To compensate for this, the calibration values were interpolated so they change smoothly from one calibration to the next. Using this interpolation, a continuous radiance estimate was obtained.
5. MEASUREMENT ANALYSES AND MODEL PREDICTIONS 5.1. Stokes parameters
Using the calibrated radiance in Equation 4, an estimate of the Stokes polarisation parameters , , is given by:
"
err
with
".=
w
(5)
z
% w w
(6)
z
v % w w
(7)
"
z
"
$ 5
and the model error err
% "
$
;+
the number of frames, the frame number and w
%
v+ w
"
v %
65 {z w w
(8)
the angle of the linear polariser for frame .
The three Stokes parameters and the error for one particular measurement are shown in Figure 4. At the moment of the measurements, a cloud was just blocking the direct sunlight. However, in the intensity image in Figure 4(a), shade still appears to be present. This so called ’shade’ appears because this surface has not been heated by the sun. The landmines are clearly visible in the intensity image. However, the tops of the landmines are much cooler than the sides that have been warmed up by the sun. When emission dominates reflection, the Stokes parameter is negative for horizontal surfaces and positive for vertical surfaces. Emission dominates reflection here, since the sky appears colder than the landmines. The Stokes parameter in Figure 4(b) shows clearly all the tops of the landmines with negative values for . Moreover, variations in the background have been surpressed. This clearly shows that for automatic detection, which is outside the scope of this paper, the Stokes parameter
is very usefull. The sides of the landmines have a positive Stokes parameter . The left side is more positive than the right side, since that side is warmer as can be seen in the intensity image. The Stokes parameter in Figure 4(c) gives less information than the previous two images. Notice that the scale of this image is a factor of 10 lower than the Stokes parameter . Mainly the heated left sides of the TM62 have a high negative radiance. This radiance is much less pronounced on the DM31, probably due to lower temperature and/or lower reflectivity. This polarised radiance is caused by a changing surface normal at the sides of the landmines.
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002 I−Parameter (133102)
7
Q−Parameter (133102) 0.02
0.92 0.9
0.01
50
0.88
[W m
0.84 150
0.82
−1
−0.01
−2
−1
sr ]
0.86
sr ]
0 100
−2
100
−0.02
150
[W m
50
−0.03
0.8 200
200 −0.04
0.78 0.76 250
−0.05
250
100
150
200
250
50
100
(a)
150
200
250
(b)
U−Parameter (133102)
−3
x 10
Stokes model error (133102) 0.018
6 50
4
0.016
50
2
−2 150
−1
sr ]
100 0.012
−2
0
0.014
[W m
100
0.01
150
[W m−2 sr−1]
50
−4 0.008
−6 200
200 0.006
−8 −10
250 50
100
150
200
250
(c)
0.004
250 50
100
150
200
250
(d)
Figure 4. The estimated Stokes images and the error in the calculation. These images are just one scene taken at 13:31 on 27 November 2001. All landmines are clearly visible in the intensity image. The scene contains plastic (TM62 and PMN) and metal landmines, with different surface characteristics. Notice that different scales have been used for each image. The mismatch between the Stokes model prediction and the actual measurement is shown in Figure 4(d). This image shows all the edges of the objects in the scene, not only the landmines, but also the poles and wires in the top right corner of the image. These edges are most probably caused by the rotation of the polarisation filter causing a small translation of the image, which is dependent on the orientation of the filter. A correction for this effect can be made by using motion correction algorithms. For the scope of this paper that is not necessary, since the edges are small and only the top of the landmine is considered.
5.2. Time analyses of the Stokes parameters
For each measurement, the Stokes parameters , and are estimated. For further analyses only the top of the landmines that are instrumented with a thermocouple are selected. Two areas of sand that also have a thermocouple in place are selected for reference as well.
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
tc25 tc24
8
tc23 tc21 tc19 tc11 tc9 tc17
tc16
Figure 5. The pixels of the landmines (in black) and the background (in gray) selected for time analyses. The criteria for selecting the top of each landmine is to select all pixels on that landmine that have a lower parameter than -0.01 Wm sr in the sequence taken at 13:38:46. This sequence is selected, because it has the highest polarisation. The selected pixels are shown in Figure 5. For this time analyses the average is taken over all the selected surface pixels of each landmine. The results of this time analyses are discussed in Section 5.4.
5.3. Model predictions
The model prediction for the Stokes parameters , and is given in Section 2. The source in this model is the sky and this radiance was measured with the hand radiometer. The temperatures of the landmine have been measured with thermo couples. The refractive indices of the material of the two AT landmines types (DM31 and TM62) are not known. This is the only free parameter in the model. The refractive index of the AP landmine was estimated before, but using the outdoor measurements this is verified again.
The following error function is minimised to find the refractive index : )v"
,
¡ 3 -
¥
)¡ 65
¢ )¡
¢ )¡
+¤£
,
3 ¡ 3
-
)¡ 65
¡
(9)
¡
with the number of measurements and and the average estimated Stokes parameters of the object (either the £ top of the landmine or the sand). The factor is included to balance the fit between the Stokes parameters and . Since the magnitude of the Stokes parameter is roughly a factor of 10 lower than the Stokes parameter , the factor was fixed at 100. )v
Now by minimising this function over the refractive index, the best fit between the prediction and the measurement was made. In the previous indoor experiment, the imaginary part of the refractive index was fixed at 0.05, since it could not be determined accurately. So for this experiment, the same assumption was made.
5.4. Results
The plots of the measured Stokes parameter and of each landmine type and the sand background as function of time are shown in Figure 6. The predicted Stokes parameters are also shown. The only parameter that was tuned for the model prediction is the refractive index , see Section 5.3. Only the measurements taken after 11:00 are taken into account, because it was noticed that in the early morning there was frozen dew on top of the landmines. The agreement between the predicted and measured Stokes parameter is reasonable. However, the measured intensity changes faster than expected. This may be explained by the way the thermo couple is attached to the surface. For the PMN it was actually mounted in contact with the rubber, but on the inside. Because of this it may take some time to heat up the thermocouple and thus the response will be slower and of lower magnitude. The measured Stokes parameter is in good agreement with the predicted Stokes parameter after 14:00. Between 11:00 and 14:00 the magnitude is about correct, but again it does not react as fast. This may also caused by the temperature measurement with the thermocouple. The magnitude of the polarisation depends on the difference between the temperature of the landmine and the temperature of the background.
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
9
The estimated refractive index for the PMN was around 1.16 with a variation of 0.01 over the three PMNs. This refractive index is much lower than the refractive index found in the indoor model verification (1.45). If this refractive index was to be used, then the Stokes parameter will be too low (up until about 14:00) and the Stokes parameter a factor three too high in magnitude. One of the explanations may be that assumption 3 (see Section 2) of the model is invalid. That means that the surface of the landmine is not strictly specular for reflection. This causes other sources to be reflected by the landmine, each with a different angle and thus canceling out some of the polarised radiance. Not fullfilling assumption number 5 (i.e., the sky radiance is polarised) may also have the effect of either increasing or this is not likely since the sky does not appear to be polarised in decreasing the measured Stokes parameter . However, MWIR for elevation angles above a few degrees. For the other assumptions, it cannot be seen how they may explain this lower than expected polarisation. The assumption in Section 4.1 that the sky is a blackbody is a likely source for the difference between the expected and measured refractive index. Simulation with Modtran with a subartic atmosphere shows that the estimated sky radiance in MWIR is much too low. A higher sky radiance would lead to a higher estimate of the refractive index.
The measured and predicted Stokes parameters and for the AT landmines of type TM62 and DM31 are shown in Figures 6(c) through (f). The measurements and predictions agree more or less equally as well as the measurements and predictions for the PMN landmine. However, the thermocouple measurement of the DM31 seems to be reacting slower. This may be caused by the big metal mass of this landmine and may be an indication that the paint reacts faster to temperature changes than the metal underneath. The refractive index for the TM62 is 1.19 and the refractive index for the DM31 is 1.15. Since refractive indices of the surfaces of these two landmines were not measured in the indoor experiment, it is impossible to state whether or not they are consistent. However, based on the different results (indoors and outdoors) of the refractive index of the PMN, it is to be expected that the true refractive indices are in actual fact higher. Again other sources of radiation combined with a not specular surface may result into a lower polarisation response and thus a lower refractive index. For the two selected sand backgrounds a similar model prediction and fit has been carried out. The match between the mea sured and predicted Stokes parameter and is very poor. This means that the model cannot be applied to sand background. The main explanation for this is that sand is a diffuse reflector and that radiance from the specular orientation has only a minor influence on the measured radiance. If one compares the -plots of the sand background with the -plots of the landmines in Figure 6, then it is clear that the polarisation of the landmine is always higher than the polarisation of the sand background. This means that on average the polarisation of the landmines is higher than the polarisation of the background. However, because of local variation in temperature and reflection, this is not valid for individual pixels: some landmine pixels can have a lower polarisation than the average background and some background pixels can have a higher polarisation than the average landmine.
For the -plots the situation is very different. The sand background has a very similar Stokes parameter compared to the tops of the landmines. It is sometimes higher and sometimes lower and quite often very close or equal to the Stokes parameter of the landmines. This is an indication that the Stokes parameter is not a good basis for reliably detecting landmines in this situation.
5.5. Image Simulations The simulator described in Section 3 was used to produce synthetic images comparable to those of Figure 4. The components of a simulated mine signature are shown in Figure 7. The results reported here are for the surrogate plastic TM62 mine with plastic case shown in Figure 2(c). The shape of that mine is well approximated by two stacked concentric cylinders of diameter 30 and 13 cm respectively. A small protuberance of diameter 1.6 cm extends roughly 2 cm above the upper cylinder. The environmental conditions simulated included direct sunlight and a cloud-free sky. (Some clouds were present during the measurement, but they did not obscure the sun.) Measured , and components appear in Figures 7(a) through 7(c), while the respective model calculations appear in Figures 7(d) through 7(f). In generating these results we have specified a relative permittivity of 1.16 for the mine casing (as noted above) and 1.6+j0.002 for the soil (as noted in a prior work ). For the surface roughness we used slope variances of zero (a smooth surface) and 0.3 for the mine casing and sand respectively. The mine simulant used in these tests has a similar painted finish on all exterior surfaces. That surface is smooth to the touch, but a diffuse component was included because of the potential for multiple scattering within the paint layer.
A study of the measured and modeled results suggests that solar irradiance has a strong role in the component via heating and also via reflection. Because of the sun angle, these contributions are strongest on the vertical faces of the mine, which were
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002 I−parameter of PMN (tc21)
10
Q−parameter of PMN (tc21) 0
1 Radiance [W m−2 sr−1]
Radiance [W m−2 sr−1]
−0.01 0.8 0.6 0.4 0.2
8
9
10
11
12
13 14 Time [h]
15
16
17
18
−0.03 −0.04 −0.05
Measured Predicted (n=1.171) 0
−0.02
−0.06
19
Measured Predicted (n=1.171) 8
9
10
11
12
(a)
13 14 Time [h]
15
16
17
18
19
(b)
I−parameter of TM62 (tc19)
Q−parameter of TM62 (tc19) 0
1 Radiance [W m−2 sr−1]
Radiance [W m−2 sr−1]
−0.01 0.8 0.6 0.4 0.2
8
9
10
11
12
13 14 Time [h]
15
16
17
18
−0.03 −0.04 −0.05
Measured Predicted (n=1.193) 0
−0.02
−0.06
19
Measured Predicted (n=1.193) 8
9
10
11
12
(c)
13 14 Time [h]
15
16
17
18
19
(d)
I−parameter of DM31 (tc16)
Q−parameter of DM31 (tc16) 0
1 Radiance [W m−2 sr−1]
Radiance [W m−2 sr−1]
−0.01 0.8 0.6 0.4 0.2
8
9
10
11
12
13 14 Time [h]
15
16
17
18
−0.03 −0.04 −0.05
Measured Predicted (n=1.157) 0
−0.02
−0.06
19
Measured Predicted (n=1.157) 8
9
10
11
12
(e)
13 14 Time [h]
15
16
17
16
17
18
19
(f)
I−parameter of sand (tc09)
Q−parameter of sand (tc09) 0
1 Radiance [W m−2 sr−1]
Radiance [W m−2 sr−1]
−0.01 0.8 0.6 0.4 0.2
−0.02 −0.03 −0.04 −0.05
Measured 0
8
9
10
11
12
13 14 Time [h]
15
16
17
18
(g)
19
Measured −0.06
8
9
10
11
12
13 14 Time [h]
15
18
19
(h)
Figure 6. The measured and predicted Stokes parameter : (a) PMN, (c) TM62, (e) DM31 and (g) sand. And the measured and predicted Stokes parameter : (b) PMN, (d) TM62, (f) DM31 and (h) sand.
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
5
5
5
10
10
10
15
15
15
20
20
20
25
25
25
30
30 5
0.75
10
0.8
15
0.85
20
0.9
0.95
25
30
1
35
1.05
40
1.1
45
1.15
50
1.2
11
30
55
5
1.25
10
15
−0.03
20
−0.02
25
30
−0.01
35
40
0
45
0.01
50
55
0.02
5
10
−20
15
−15
20
25
30
−10
35
40
−5
45
0
50
55
5 −3
x 10
(a) Measured Stokes parameter ¦ .
(b) Measured Stokes parameter § .
(c) Measured Stokes parameter ¨ .
40
40
40
35
35
35
30
30
30
25
25
25
20
20
20
15
15
15
10
10
10
5
5
5
0.1
10
0.2
15
0.3
20
0.4
25
0.5
30
35
0.6
40
0.7
5
5
−0.015
10
−0.01
15
20
−0.005
0
25
0.005
30
35
0.01
40
0.015
5
−10
10
−8
15
−6
20
−4
25
30
−2
35
0
40
2 −3
x 10
(d) Calculated Stokes parameter ¦ .
(e) Calculated Stokes parameter § .
(f) Calculated Stokes parameter ¨ .
Figure 7. Comparison of measured and simulated polarimetric data. not examined in the specular model described previously. Conversely, thermal and sky radiation are the dominant contributors to the and components. Shadowing of the solar component is clearly evident in the image, but not in the and results. The measured and components show evidence for strong depolarization at single pixels near the specular points on the upper cylinder, which may be the result of edge diffraction by the solar component (a contribution not included in the model). While these modeling results reproduce a number of the features found in the measurements, the need for more work is evident. Among the differences, we note an apparent reflection of the upper cylinder in the surface of the lower cylinder and a possible reflection of the soil in the side wall of the lower cylinder. Those doubly-reflected components are not currently included in the simulation.
6. CONCLUSIONS AND DISCUSSION In this paper our specular IR polarisation model was verified in an outdoor test, using a polarimetric camera and additional measurements (meteorological data, thermocouples). In Section 5.4, it is shown that the model performs a reasonable prediction of both the (normal IR) intensity and the polarisation (specificly the Stokes parameter ). The refractive index is the only parameter used to fit the model to the measured data. The refractive index for the PMN landmine was 1.16, for the TM62 landmine 1.19 and for the DM31 landmine 1.16. The refractive index found for the PMN landmine differs from the previous value of 1.45 obtained in the indoor experiment. This difference may be explained by surface roughness of the landmines combined with the presence of other sources (sun and sky) or a difference between the real and estimated sky radiance. In this paper it has also been shown that passive IR polarisation is of value for detection of landmines under a variety of circumstances (freezing temperatures, sun, clouds and even a small amount of precipitation). Furthermore, under these circumstances the polarimetric component gives a more robust image of the tops of the landmines than normal IR images. Automatic detection of landmines in IR images may be improved with this additional polarimetric component. Images formed from measured Stokes parameters were also compared with the results of a combined thermal-radiometric image simulator. Although the measurements show evidence for a number of phenomena not currently included in the model, the simulation is largely in qualitative agreement with the measurements.
Preprint Proc. SPIE Vol. 4742, Det. and Rem. Techn. for Mines and Minelike Targets VII, Orlando FL, USA, Apr. 2002
12
ACKNOWLEDGEMENTS The work presented in this paper is sponsored by the Netherlands Ministry of Defense. The efforts of BAB and JTJ were supported in part by funds from Duke University under an award from the ARO (the OSD MURI program). The findings, opinions and recommendations expressed therein are those of the author and are not necessarily those of Duke University or the ARO.
REFERENCES 1. F. Cremer, W. de Jong, and K. Schutte. Infrared polarisation measurements and modelling applied to surface laid antipersonnel landmines. Optical Engineering, May 2002. To be published. 2. F. Cremer, W. de Jong, and K. Schutte. Infrared polarisation measurements of surface and buried anti-personnel landmines. In A. C. Dubey, J. F. Harvey, J. T. Broach, and V. George, editors, Proc. SPIE Vol. 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, pages 164–175, Orlando (FL), USA, Apr. 2001. 3. F. Cremer, K. Schutte, J. G. M. Schavemaker, and E. den Breejen. A comparision of decision-level sensor-fusion methods for anti-personnel landmine detection. Information Fusion, 2(3):187–208, Sept. 2001. 4. I. K. S¸end¨ur and B. A. Baertlein. Numerical simulation of thermal signatures of buried mines over a diurnal cycle. In A. C. Dubey, J. F. Harvey, J. T. Broach, and R. E. Dugan, editors, Proc. SPIE Vol. 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, pages 156–167, Orlando (FL), USA, Apr. 2000. 5. E. Hecht. Optics. Addison-Wesley publishing company, Reading (MA), USA, second edition, 1987. 6. B. A. Baertlein, J. T. Johnson, and W. J. Liao. Model-based surface mine detection in polarimetric IR imagery. In J. T. Broach, R. S. Harmon, and G. J. Dobeck, editors, Proc. SPIE Vol. 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, Orlando (FL), USA, Apr. 2002. This volume. 7. L. Tsang, J. A. Kong, and R. T. Shin. Theory of Microwave Remote Sensing. Wiley-Interscience, New York, NY, 1985. 8. P. Beckmann and A. Spizzichino. The Scattering of Electromagnetic Waves From Rough Surfaces. Artech House, Norwood, MA, 1987. 9. W. de Jong, H. A. Lensen, and Y. H. L. Janssen. Sophisticated test facility to detect land mines. In A. C. Dubey and J. F. Harvey, editors, Proc. SPIE Vol. 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, pages 1409–1418, Orlando (FL), USA, Apr. 1999. 10. W. de Jong, F. Cremer, K. Schutte, and J. Storm. Usage of polarisation features of landmines for improved automatic detection. In A. C. Dubey, J. F. Harvey, J. T. Broach, and R. E. Dugan, editors, Proc. SPIE Vol. 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, pages 241–252, Orlando (FL), USA, Apr. 2000. 11. F. Cremer, P. B. W. Schwering, W. de Jong, and K. Schutte. Infrared polarisation measurements of targets and backgrounds in a marine environment. In W. R. Watkins, D. Clement, and W. R. Reynolds, editors, Proc. SPIE Vol. 4370, Targets and Backgrounds VII: Characterization and Representation, pages 169–180, Orlando (FL), USA, Apr. 2001. 12. I. K. S¸end¨ur, J. T. Johnson, and B. A. Baertlein. Analysis of polarimetric IR phenoma for detection of surface mines. In A. C. Dubey, J. F. Harvey, J. T. Broach, and V. George, editors, Proc. SPIE Vol. 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, pages 153–163, Orlando (FL), USA, Apr. 2001.
