Comparison of the Inversion Periods for Polarimetric and 

Conventional Long-wave IR (LWIR) Imagery 

by M. Felton, K. P. Gurton, L. E. Roth, J. L. Pezzaniti, and D. B. Chenault

ARL-TR-4931 August 2009 

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Army Research Laboratory 

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ARL-TR-4931 August 2009 

Comparison of the Inversion Periods for Polarimetric and 

Conventional Long-wave IR (LWIR) Imagery 

M. Felton and K. P. Gurton 

Computational and Information Sciences Directorate, ARL 

L. E. Roth 

U.S. Army Armament Research and Development Engineering Center 

J. L. Pezzaniti and D. B. Chenault 

Polaris Sensor Technologies, Inc. 

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Comparison of the Inversion Periods for Polarimetric and Conventional Longwave IR (LWIR) Imagery 

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14. ABSTRACT 

We report the results of a multi-day diurnal study in which radiometrically calibrated polarimetric and conventional thermal 

imagery is recorded in the long-wave IR (LWIR) to identify/compare the respective time periods in which minimum target 

contrast is achieved, e.g., thermal inversion periods are typically experienced during dusk and dawn. Imagery is recorded with 

a polarimetric IR sensor employing a 324x256 microbolometer array using a spinning achromatic retarder to perform the 

polarimetric filtering. The images used in this study include the S0, normalized S1, and normalized S2 Stokes images and the 

degree of linear polarization (DOLP) images of a scene containing military vehicles and the natural background. In addition, 

relevant meteorological parameters measured during the test period include air temperature, ambient loading in the LWIR, 

relative humidity, and cloud cover, height and density. The data shows that the chief factors affecting polarimetric contrast 

are the amount of thermal emission from the objects in the scene and the abundance of LWIR sources in the optical 

background. In addition, we found that contrast between targets and background within polarimetric images often remains 

relatively high during periods of low thermal contrast. 

15. SUBJECT TERMS

Polarimetric imaging, thermal imaging, microbolometer, target contrast, inversion periods 

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Contents 

List of Figures iv

List of Tables v

Acknowledgments vi

1. Introduction 1

1.1 Thermal Polarimetric Imaging ........................................................................................1 

2. Experiment 2

2.1 Polarimetric Sensor .........................................................................................................2 

2.2 Field Test Site..................................................................................................................4 

2.3 Image Analysis................................................................................................................6 

3. Results 7

3.1 Scene in Figure 4a ...........................................................................................................8 

3.2 Scene in Figure 4b.........................................................................................................12 

4. Conclusions 15

5. References 17

List of Symbols, Abbreviations, and Acronyms 18

Distribution List 19 

iii 
List of Figures 

Figure 1. a. Image of polarimetric sensor and b. optical layout of the spinning retarder, 

microbolometer-based sensor. ...................................................................................................3 

Figure 2. Precision Armaments Laboratory tower with elevator that housed the polarimetric 

sensor situated on the sixth floor. ..............................................................................................5 

Figure 3. Target site consisting of two military vehicles and a natural background. The test 

was carried out May 12–15, 2009, but this image was taken in the preceding fall. 

Therefore, during the test, the grass was alive and thick and the bushes and trees 

contained leaves.........................................................................................................................5 

Figure 4. a. Target 1 and its natural background. The target, grass, and trees regions of 

interest correspond to the blue, red, and green boxes, respectively. b. Target 2 and its 

natural background. The back and top of the target and the grass regions of interest 

correspond to the blue, green, and red boxes, respectively. ......................................................7 

Figure 5. Example contrast values comparing the target to the grass and the corresponding S0

image taken at a. 07:00, b. 11:00, and c. 19:00..........................................................................7 

Figure 6. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass 

and trees, on May 12, 2009........................................................................................................8 

Figure 7. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass 

and trees, on May 13, 2009........................................................................................................9 

Figure 8. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass 

and trees, on May 14, 2009........................................................................................................9 

Figure 9. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass 

and trees, on May 15, 2009......................................................................................................10 

Figure 10. a. S0 image of the target site at 10:00 on May 13 with the truck introduced into 

the scene. b. Normalized S1 image of the upper target at 09:30 before the truck was 

introduced into the scene. c. Normalized S1 image of the upper target at 10:00 after the 

truck was introduced into the scene.........................................................................................11 

Figure 11. Diurnal contrast for the target in figure 4b and its background of grass on May 12, 

2009..........................................................................................................................................13 

Figure 12. Diurnal contrast for the target in figure 4b and its background of grass on May 13, 

2009..........................................................................................................................................13 

Figure 13. Diurnal contrast for the target in figure 4b and its background of grass on May 14, 

2009..........................................................................................................................................14 

Figure 14. Diurnal contrast for the target in figure 4b and its background of grass on May 15, 

2009..........................................................................................................................................14 

iv 
List of Tables 

Table 1. Specifications for the polarimetric sensor. .......................................................................3 

Table 2. Polarimetric sensor data products.....................................................................................4 

v 
vi 

Acknowledgments 

This work was accomplished using data from the Hyperspectral and Polarimetric Target 

Detection Program at the Precision Armament Laboratory (PAL) Tower at the Armament 

Research and Development Engineering Center (ARDEC) with the help of Mr. Joao Romano 

(Program Manager) and Mr. Mark Woolley (PAL Manager). 
1. Introduction 

Thermal imagers have been established as the primary tool used in military and security 

activities that involve surveillance, targeting and tracking, and nighttime operations. Unlike 

image intensification (I2

) devices, which depend on ambient light levels, thermal imagers exploit 

the fact that all objects with a temperature above absolute 0 K emit thermal radiation by creating 

a pseudo image of the scene based on this thermal emission. The two thermal imaging windows 

are the mid-infrared (IR), 3–5 μm, and the long-wave IR (LWIR), 8–14 μm, both chosen for the 

relatively low amounts of absorption from atmospheric species such as carbon dioxide (CO2) and 

water (H2O). 

Contrast between the objects within a thermal image is determined by their effective 

temperatures, which are a function of their true temperature and their emissivity, a characteristic 

of an object that describes how efficiently it radiates absorbed energy as compared to a 

blackbody. If there is no thermal contrast between a target and its background, it cannot be seen 

in a thermal image. The diurnal cycles of the thermal properties of both manmade and natural 

objects tend to bring about periods of low contrast within thermal images, often referred to as 

thermal crossovers or inversion periods (1). These inversion periods tend to occur during 

periods of rapidly changing temperatures, such as sunrise and sunset, but may occur at any time 

throughout the day, depending both on temperature differences between objects and their 

backgrounds and environmental factors such as solar loading. A technology that enhances 

conventional thermal imaging should extend its operational ability into these periods of thermal 

inversion. 

1.1 Thermal Polarimetric Imaging 

Thermal polarimetric imaging has been proposed as a method to enhance conventional thermal 

imaging (2). It creates images of a scene that are based on the states of polarization of the IR 

light emitted or reflected from the objects within the scene. An object’s polarimetric signature is 

a function of its surface geometry and roughness. Due to the different geometrical and 

roughness features of objects constituting the natural background and manmade objects, the 

polarization states of the emitted and reflected thermal light can be used as a discriminator 

between objects of interest and background clutter. By using the additional information in the 

form of polarization, it is possible to obtain an image of a scene that has polarimetric contrast 

between objects and their backgrounds, despite the fact that there is no thermal contrast. 

Just as there are environmental factors that affect thermal contrast, there are also environmental 

factors that affect polarimetric contrast. The most significant of these factors involves sources of 

IR radiation in what has been referred to as the optical background, which is defined as the 

sources of IR radiation that are not necessarily visible within the scene but still reflect IR 

1 
radiation off of an object and into the field of view (FOV) of the camera (3). The optical 

background can become partially polarized upon reflection and act as a competing component to 

the emitted polarized light. Polarized emitted and reflected light are often orthogonal to each 

other, because the emitted polarized light tends to be in the direction parallel to the surface 

normal while light polarized upon reflection tends to be in the direction parallel to the reflecting 

surface. When the two superpose, the effect can be a reduction in the magnitude of the 

polarimetric signature of an object. Potential sources of optical background radiation may 

include vehicles, buildings, trees, clouds, and water vapor. 

This study compares the temporal occurrence of conventional thermal inversion periods to that 

of polarimetric and correlates these inversions with environmental factors. If there are 

significant periods of time in which contrast within polarimetric images are present while 

conventional thermal contrast has been lost, then it can be concluded that polarimetric imaging 

can enhance conventional thermal imaging. 

2. Experiment 

2.1 Polarimetric Sensor 

The sensor used is a LWIR microbolometer-based rotating retarder imaging polarimeter 

developed by Polaris Sensor Technologies, Inc., Huntsville, AL (figure 1) (4). It operates by 

capturing images sequentially in time, each at a different orientation of the rotating retarder. 

Together, the retarder and linear polarizer act as a polarization state analyzer for the light 

forming the image. Using the data reduction matrix method, the Stokes vectors are calculated, 

which completely characterize the polarization states of the light from the scene. Table 1 lists 

the sensor specifications and table 2 provides definitions of the data products used in this study. 

2 
Figure 1. a. Image of polarimetric sensor and b. optical layout of the spinning retarder, 

microbolometer-based sensor. 

Table 1. Specifications for the polarimetric sensor. 

Parameter Value 

Waveband 7.5–13.5 μm 

FOV 13.7° x 11.0° 

Objective focal length 50 mm 

Total FPA size 324 x 256 

Frame rate 30 frames per second 

Note: FPA = focal plane array 

3 
Table 2. Polarimetric sensor data products. 

Measured image Description 

S0 Radiance image, W/cm2-sr 

S1 Horizontally polarized radiance minus vertically polarized radiance, W/cm2-sr 

S1/S0 S1 image normalized by radiance image, S0

S2 45° polarized radiance minus 135° polarized radiance, W/cm2-sr 

S2/ S0 S2 image normalized by radiance image, S0

DOLP 

Degree of linear polarization, 

0

2

2

2

1

S

 SS

A four-point non-uniformity correction was done to correct for non-uniformities in the FPA and 

slight nonlinearity in the sensor response. The temperatures were chosen to encompass the range 

of temperatures of the objects in the scene that were expected to be encountered during the study. 

The polarimetric calibration was carried out using a wire-grid polarizer on a barium fluoride 

(BaF2) substrate that was back illuminated by a blackbody source and canted at an angle so that 

it was front illuminated by a large roughened black plate uniformly at room temperature so that 

reflected radiance from the face of the polarizer was kept constant. 

2.2 Field Test Site 

The test was conducted at the Precision Armaments Laboratory located in Picatinny Arsenal, NJ. 

The camera was situated on the sixth floor of a tower (approximately 200 ft high) looking out 

of the windows towards the target site, which was at approximately 0.5 km in range and 

consisted of two military vehicles and natural backgrounds including grass, brush, and trees 

(figures 2 and 3). In addition to the 200-ft elevation of the sixth floor of the tower, the tower 

itself was situated on top of a ridge approximately 150 ft above the target site. 

4 
Figure 2. Precision Armaments Laboratory tower with elevator that 

housed the polarimetric sensor situated on the sixth floor. 

Figure 3. Target site consisting of two military vehicles and a natural background. 

The test was carried out May 12–15, 2009, but this image was taken in the 

preceding fall. Therefore, during the test, the grass was alive and thick and 

the bushes and trees contained leaves. 

5 
Environmental measurements available at the target site include air temperature, relative 

humidity, ceilometer data, and pyrgeometer (precision infrared radiometer [PIR]) data. The 

ceilometer provides real-time reports of cloud bases and depths directly above the ceilometer and 

determines cloud cover by using a weighted average of 30-s cloud hit reports over a 30-min 

period. The pyrgeometer measured ambient LWIR radiation from 3–50 μm within a 2π

steradian FOV. 

The data acquisition clocks for the environmental data and the camera were synced to ensure 

coincident data. The temporal resolution of the data are as follows: air temperature, relative 

humidity and pyrgeometer – 2 s; ceilometer – 10 s; and camera – 10 min. Data was acquired 

continuously beginning at 10:00 on May 12, 2009, and ended at 09:00 on May 15, 2009. 

2.3 Image Analysis 

A contrast-based study was performed between the military vehicles and their immediate 

backgrounds. The data products used in this study include S0, normalized S1, normalized S2, and 

degree of linear polarization (DOLP). Because some of these data products are measured in 

different units and are of different magnitudes, the analysis was performed on standardized 

versions of these images (5, 6). In other words, each image had its mean subtracted and was 

normalized by its standard deviation. This makes it possible to directly compare the contrasts for 

all of the data products. Using temporal sequences of co-registered images, regions of interest 

were defined for the targets and their backgrounds and the mean values were calculated for each 

data product at each time in the series. The contrast is defined as the absolute value of the 

difference between the mean target value,  t

 , and the mean background value,  b , where the 

prime indicates that the means were calculated using standardized images: 

bt contrast      . (1) 

Because standardizing the images gives them unitless values, the contrast defined by equation 1 

is also unitless. 

Data for two targets (military vehicles) are used in the analysis. One target was directly facing 

the tower while the other was oriented at a 45° angle relative to the tower (figure 4). These two 

orientations required different strategies for choosing the regions of interest. Most of the 

surfaces of the target in figure 4a are oriented in the same direction relative to the FOV of the 

camera. Therefore, widely varying magnitudes of polarimetric signatures due to diversity of 

surface geometry minimally affect the target’s mean signatures. On the other hand, the target 

oriented at 45° (figure 4b) has multiple surfaces oriented in different directions, and therefore, 

each one preferentially produces states of polarization that can lead to misleading signatures 

when averaged together. For this target, two separate regions of interest were used, each 

corresponding to a prominent surface. 

6 
Figure 4. a. Target 1 and its natural background. The target, grass, and trees regions of interest correspond to 

the blue, red, and green boxes, respectively. b. Target 2 and its natural background. The back and 

top of the target and the grass regions of interest correspond to the blue, green, and red boxes, 

respectively. 

Examples of contrast values calculated using equation 1 and their corresponding images are 

shown in figure 5. The contrasts were calculated for the S0 images of the scene in figure 4a 

taken at three different times of the day. Because this scene has two different natural 

backgrounds, one is chosen at a time to calculate the contrast between the target and the chosen 

background. In the case of figure 5, the grass background is used to calculate the contrast. 

Figure 5. Example contrast values comparing the target to the grass and the 

corresponding S0 image taken at a. 07:00, b. 11:00, and c. 19:00. 

3. Results 

The results are presented in the form of diurnal contrast plots (calculated using equation 1) and 

the corresponding environmental data. The ceilometer data is omitted because the cloud 

information can be obtained from the pyrgeometer (PIR) data. A direct comparison of 

ceilometer and pyrgeometer data revealed that the baseline reading for a cloudless daytime sky 

7 
was roughly 275–280 W/m2

. Any value higher than this typically indicates the presence of 

clouds such that the higher the value, the thicker the cloud cover. During this study, sunrise and 

sunset occurred at roughly 05:00 and 20:00, respectively. 

3.1 Scene in Figure 4a 

Figures 6–9 present the diurnal contrast for the target in figure 4a against its background of grass 

and trees for May 12, 13, 14, and 15, 2009, respectively. For this scene, it was consistently 

found that DOLP is dominated by S1 because the magnitude of S2 was generally half that of S1. 

Therefore, the DOLP contrast often mimics the S1 contrast. On the other hand, the S2 contrast 

was found to mimic the S0 contrast. From the FOV of the sensor, there are not many facets of 

the target in figure 4a that are capable of producing large amounts of 45° or 135° polarized light, 

namely surfaces oriented at 45° or 135° with the cold sky as an optical background. Therefore, 

the S2 measured by the sensor is mainly due to emission and affected by cloud cover to a lesser 

extent than S1 and DOLP. 

Figure 6. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass and trees, 

on May 12, 2009. 

8 
Figure 7. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass and trees, 

on May 13, 2009. 

Figure 8. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass and trees, 

on May 14, 2009. 

9 
Figure 9. Diurnal contrast for the target in figure 4a compared to its two backgrounds, grass and trees, 

on May 15, 2009. 

May 12 and 13 were similar days in that the morning skies were clear while the afternoon skies 

were partly cloudy at times. In addition, the temperature and relative humidity were inversely 

proportional to each other on both days. The S0 and S2 contrasts were best at higher 

temperatures. Thermal inversions occurred during times of rapidly changing temperatures in the 

morning and at night. At 10:00 on May 12 and 07:00, 10:00, and 23:00 on May 13, there were 

thermal inversions between the target and the grass, as can be seen from the S0 contrast curves in 

figures 6 and 7 during these times. In additional, thermal inversion periods were experienced 

between the target and the trees at 22:00 on May 12 and at 08:00 and 21:00 on May 13. With the 

exception of 23:00 on May 13 between the target and the grass, polarimetric contrast, namely in 

S1 and DOLP, remained high during the periods of thermal inversion that occurred on the first 

two days of the test. The primary factor effecting S1 and DOLP contrast was sources of LWIR 

optical background, which came in two forms: the presence of clouds in the sky and the 

presence of a truck that was temporarily driven into the scene. Inversions correlated to cloud 

cover occurred in these data products on May 12, while the presence of clouds had a milder 

impact on May 23, often reducing contrast as opposed to eliminating it. Reduced contrast in S1

and DOLP due to the truck is evident at 10:40 on May 12 and from 09:50–10:50 on May 13. 

The truck was situated approximately 15 ft in front and to the right of the target. Extra care was 

taken to ensure that the regions of interest for the grass did not include any portion of the truck. 

Figure 10a shows the S0 image of the target site at 10:00, which is after the truck has been 

introduced into the scene. Figure 10b and c are normalized S1 images that focus on the upper 

target at 09:30 (before the truck was introduced into the scene) and 10:00 (after the truck was 

10 
introduced into the scene), respectively. The black, rectangular shape in the bottom right-hand 

corner of figure 10c is the back portion of the top of the truck. These images are displayed on 

the same scale and illustrate the decreases in contrast in the normalized S1 images between the 

target and its background as shown in the S1 curve of figure 7, namely during the time period 

09:50–10:50. 

Figure 10. a. S0 image of the target site at 10:00 on May 13 with the truck introduced 

into the scene. b. Normalized S1 image of the upper target at 09:30 before 

the truck was introduced into the scene. c. Normalized S1 image of the upper 

target at 10:00 after the truck was introduced into the scene. 

Inclement weather moved into the Picatinny area in the early morning hours of May 14 and there 

were intermittent periods of light rain up until the early afternoon. During this time, there was 

thick cloud cover as reflected in the PIR plot of figure 8. The fluctuations in thermal contrast are 

caused by the periods of light rain, which has the effect of wetting the surfaces in the scene 

causing them all to appear the same temperature. In addition, there was a slight break in the 

cloud cover at 15:00, which caused the scene to heat. The S0 contrast initially is reduced, as the 

target and its backgrounds approach the same temperature due to different rates of heating. 

Eventually, the S0 contrast improves as the effective temperatures of the target and its 

11 
backgrounds reach their respective levels. This same lag in improved contrast is not experienced 

by the polarimetric data products, because their contrast mainly depends on the optical 

background. As can be seen from the PIR plot at 15:00 in figure 8, there is a slight reduction in 

ambient LWIR due to a break in the clouds, resulting in a temporary improvement in the contrast 

of the polarimetric data products. Conditions remained the same throughout the night and 

through the next morning. Contrast remains low in all data products due to the static 

temperatures and constant cloud cover until sunrise just before 05:00 when there was a slight 

break in the clouds. Temporary contrast appears between the target and the grass in all data 

products and only in S1 and DOLP between the target and the trees. At 05:30, thick fog 

descended onto the target site until just after 08:00. During this time, all contrast was lost. 

3.2 Scene in Figure 4b 

Figures 11–14 present the diurnal contrast for the target in figure 4b against its background of 

grass for May 12, 13, 14, and 15, 2009, respectively. Recall that the contrasts were calculated 

using two different prominent facets of the target in figure 4b, as opposed to the entire target, so 

that cancellation effects could be kept to a minimum. Unlike the target in figure 4a, the target in 

figure 4b has a facet that is oriented in such a way that it is capable of producing larger amounts 

of S2, namely the back of the target. This is reflected in the contrast plots in figures 11 and 12. 

On both of these days, the contrast for the back of the target is greatest in the S2 images during 

clear sky conditions in the morning hours when the back of the target is receiving more direct 

illumination from the sun while reflecting the cold sky. When clouds enter the sky, the S2

contrast for the back of the tank is reduced. The S1 and DOLP contrast for the back of the target 

is generally lower than the S2 contrast, while the S0 contrast was the highest in the afternoons on 

both days. 

12 
Figure 11. Diurnal contrast for the target in figure 4b and its background of grass on May 12, 2009. 

Figure 12. Diurnal contrast for the target in figure 4b and its background of grass on May 13, 2009. 

13 
Figure 13. Diurnal contrast for the target in figure 4b and its background of grass on May 14, 2009. 

Figure 14. Diurnal contrast for the target in figure 4b and its background of grass on May 15, 2009. 

On May 12, the contrast for the top of the target in the S1 and DOLP images was much higher 

than for S0 and S2 under clear sky conditions. The presence of clouds acted as a source of LWIR 

and therefore produced a reflected component off of the target that competed with the target’s 

14 
emitted S1 and DOLP signatures. On May 13, the contrast in the S1 and DOLP images were 

consistently higher than that of S0 and S2. The contrast was again modulated by the presence of 

clouds but to a lesser extent than on the previous day. Most importantly, during the period of 

low thermal contrast observed from 00:00 to 09:00 on May 13, there was significant contrast in 

the S1 and DOLP images. 

The effect of the thick cloud cover and periods of light rain on May 14 can be seen in figure 13 

for the back of the target as the overall poor contrast in all polarimetric images and the 

intermittent contrast present thermally. The S2 contrast peaks at 15:00 at the exact time that 

there is a break in the clouds, indicating that the lack of contrast was due to the LWIR optical 

background provided by the clouds. Similarly, for the top of the target, each time there was a 

slight break in the clouds, i.e., 00:00–04:00 and 15:00, there was a return of the contrast to the S1

and DOLP images. The results were similar during the conditions of thick cloud cover and fog 

experienced on the morning of May 15. The slight break in the clouds around sunrise allowed 

contrast to briefly return in the S0 images of the back of the target and in all of the images for the 

top of the target. The fog eventually sets in and destroys all contrasts. 

4. Conclusions 

In this study, the temporal occurrence of conventional thermal and polarimetric inversions was 

examined as well as their correlations to environmental factors. Imagery was recorded with a 

polarimetric IR sensor employing a 324x256 microbolometer array using a spinning achromatic 

retarder to perform the polarimetric filtering. The images used in this study included the S0, 

normalized S1, and normalized S2 Stokes images and the DOLP images of scenes containing 

military vehicles and the natural background. In addition, relevant meteorological parameters 

measured during the test periods included air temperature, ambient loading in the LWIR, relative 

humidity, and cloud cover, height and density. 

This study revealed that during most thermal inversion periods, there remains polarimetric 

contrast between at least some facet of the target, and in many cases, the whole target, and its 

background. In addition, the data shows that the chief factors affecting polarimetric contrast are 

the amount of thermal emission from the objects in the scene and the abundance of LWIR 

sources in the optical background. 

These findings suggest that conventional thermal imagery can be enhanced by incorporating 

polarimetric information. The typical thermal inversions can affect contrast for up to 2 h at a 

time, reducing their effectivity and jeopardizing the success of military and security missions. 

The ability of polarimetric imaging to enhance conventional thermal imaging is limited by 

susceptibility to the optical background. If a polarmetrically enhanced thermal imager is being 

used during a mission under conditions in which there are significant optical background 

15 
sources, then the performance of the system will reduce to that of a conventional thermal system. 

In the absence of sources of LWIR optical background, the system will greatly improve the 

detection of objects of interest against natural backgrounds. 

16 
5. References 

1. Shumaker, D. L.; Wood, J. T.; Thacker, C. R. Infrared Imaging Systems Analysis; DCS 

Corporation, Alexandria, VA, 1993, 2-45–2-49. 

2. Tyo, J. S.; Goldstein, D. L.; Chenault, D. B.; Shaw, J. A. Review of Passive Imaging 

Polarimetry for Remote Sensing Applications. App. Optics 2006, 45 (22), 5453–5469. 

3. Tyo, J. S.; Ratliff, B. M.; Boger, J. K.; Black, W. T.; Bowers, D. L.; Fetrow, M. P. The 

Effects of Thermal Equilibrium and Contrast in LWIR Polarimetric Images. Optics Exp.

2007, 15 (23), 15161–15167. 

4. Pezzaniti, J. L.; Hyatt, B.; Reinhardt, J. Systems Users Manual: LWIR Rotating Retarder 

Imaging Polarimeter, 2008. 

5. Dillon, W. R.; Goldstein, M. Multivariate Analysis: Methods and Applications; John Wiley 

& Sons, New York, Chichester, Brisbane, Toronto, and Singapore, 10–13, 1984. 

6. Felton, M.; Gurton, K. P.; Ligon, D.; Raglin, A. Discrimination of Objects Within 

Polarimetric Imagery Using Principle Component and Cluster Analysis; ARL-TR-4216; 

U.S. Army Research Laboratory: Adelphi, MD, 2007. 

17 
18 

List of Symbols, Abbreviations, and Acronyms 

ARDEC Armament Research and Development Engineering Center 

BaF2 barium fluoride 

CO2 carbon dioxide 

DOLP degree of linear polarization 

FOV field of view 

H2O water 

I

2 image intensification 

IR infrared 

LWIR long-wave IR 

PAL Precision Armament Laboratory 

PIR precision infrared radiometer 
NO. OF 

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 ELEC DEFNS TECHL INFO CTR 

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 8725 JOHN J KINGMAN RD STE 0944 

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 3701 N FAIRFAX DR 

 ARLINGTON VA 22203-1714 

 1 CD OFC OF THE SECY OF DEFNS 

 ATTN ODDRE (R&AT) 

 THE PENTAGON 

 WASHINGTON DC 20301-3080 

 1 US ARMY ABERDEEN TEST CENTER 

 ATTN TEDT AT WFT F CARLEN 

 400 COLLERAN ROAD 

 ABERDEEN PROVING GROUND MD 

 21005-5009 

 1 US ARMY ARMAMENT RSRCH AND 

 DEVELOPMENT ENGINEERING CTR 

 ATTN AMSRD AAR MEF S L E ROTH 

 BLDG 407 

 PICATINNY ARSENAL NJ 07806-5000 

 1 US ARMY CECOM RDEC NVESD 

 ATTN AMSRD CER NV ST SIP 

 J HOWE 

 10221 BURBECK RD STE 430 

 FT BELVOIR VA 22060-5806 

 1 US ARMY INFO SYS ENGRG CMND 

 ATTN AMSEL IE TD A RIVERA 

 FT HUACHUCA AZ 85613-5300 

 1 COMMANDER 

 US ARMY RDECOM 

 ATTN AMSRD AMR W C MCCORKLE 

 5400 FOWLER RD 

 REDSTONE ARSENAL AL 35898-5000 

 1 US ARMY RDECOM ARDEC 

 ATTN AMSRD AAR AEP S 

 J ROMANO 

 BLDG 407 

 PICATINNY ARSENAL NJ 07806-5000 

NO. OF 

COPIES ORGANIZATION 

 1 US ARMY TARDEC 

 ATTN AMSRD TAR MS 263 

 G GRANT 

 6501 EAST 11 MILE RD 

 WARREN MI 48397-5000 

 1 AFRL/RYJT 

 ATTN R T MACK 

 2241 AVIONICS CIRCLE BLDG 620 

 WRIGHT-PATTERSON AFB OH 

 45433-7320 

 1 AFRL/VS 

 ATTN T R CAUDILL 

 3550 ABERDEEN AVE SE BLDG 423 

 KIRTLAND AFB NM 87117 

 1 AFRL/VSS 

 ATTN M J DUGGIN 

 3550 ABERDEEN AVE SE 

 KIRTLAND AFB NM 87117 

 1 USAFRL-VSSS 

 ATTN M FETROW 

 3550 ABERDEEN SE 

 KIRTLAND AFB NM 87117 

 1 JOHNS HOPKINS UNIVERSITY 

 APPLIED PHYSICS LAB 

 ATTN MP3-W110 A GOLDBERG 

 11100 JOHNS HOPKINS RD 

 LAUREL MD 20723-6099 

 1 UNIVERSITY OF ARIZONA 

 COLLEGE OF OPTICAL SENSORS 

 ATTN S TYO 

 TUCSON AZ 85721 

 1 MONTANA STATE UNIVERSITY 

 ATTN J SHAW 

 108 CULBERTSON HALL 

 P.O. BOX 172000 

 BOZEMAN MT 59717-2000 

 1 UNIVERSITY OF ARIZONA 

 ATTN COLLEGE OF OPTICAL 

 SCIENCES B M RATLIFF 

 TUSCON AZ 85721 

19 
20 

NO. OF 

COPIES ORGANIZATION 

 2 POLARIS SENSOR TECHNOLOGIES 

 INC 

 ATTN D B CHENAULT 

 ATTN J L PEZZANITI 

 200 WESTSIDE SQUARE STE 320 

 HUNTSVILLE AL 35801 

 1 US ARMY RSRCH LAB 

 ATTN RDRL CIM G T LANDFRIED 

 BLDG 4600 

 ABERDEEN PROVING GROUND MD 

 21005-5066 

 15 US ARMY RSRCH LAB 

 ATTN IMNE ALC HRR MAIL & 

 RECORDS MGMT 

 ATTN RDRL CIE M FELTON 

 (5 copies) 

 ATTN RDRL CIE S K GURTON 

 (5 copies) 

 ATTN RDRL CIM L TECHL LIB 

 ATTN RDRL CIM P TECHL PUB 

 ATTN RDRL SEE E K KLETT 

 ATTN RDRL SES E D ROSARIO 

TOTAL: 36 (1 ELEC, 1 CDS, 34 HCs)