ir composite image generation by wavelength band based on

Research ArticleIR Composite Image Generation by Wavelength Band Based onTemperature Synthesis Estimated from IR Target Signature andBackground Scene

Tae Wuk Bae ,1 Young Choon Kim ,2 and Sang Ho Ahn3

1Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute, Daegu 42994, Republic of Korea2Department of Information and Communication Security, U1 University, Asan 31415, Republic of Korea3Department of Electronic Telecommunications, Mechanical & Automotive Engineering, Inje University,Gimhae 50834, Republic of Korea

Correspondence should be addressed to Young Choon Kim; [email protected]

Received 12 October 2019; Accepted 7 November 2019; Published 10 December 2019

Academic Editor: Yasuko Y. Maruo

Copyright © 2019 Tae Wuk Bae et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Infrared (IR) target signatures and background scenes are mainly used for military research purposes such as reconnaissance anddetection of enemy targets in modern IR imaging systems like IR search and track (IRST) system. For understanding and analyzingIR signatures and backgrounds in the IR imaging systems, an IR wavelength band (WB) conversion which transforms an arbitraryWB image to another WB is very important in the absence of equipment by WB. In addition, IR image synthesis of targets andbackgrounds can provide a great deal of information in the IR target detection field. However, the WB conversion is actually avery challenging research due to lack of information on the absorptivity and transmittance of enormous components of anobject or atmosphere. In addition, the radiation and reflectance characteristics of short-wave IR (SWIR)-WB are very differentfrom those of long-wave IR (LWIR)-WB and middle-wave IR (MWIR)-WB. Therefore, the WB conversion in this paper islimited only to IR target signatures and monotonous backgrounds, which is commonly used for military purposes, at a longdistance. This paper proposes an IR synthesis method for generating a synthesized IR image of three IR-WBs by synthesizingan IR target signature and a real background scene for an arbitrary IR-WB. In the proposed method, each temperatureinformation is first estimated from an IR target signature and IR background image for an arbitrary IR-WB, and then asynthesized temperature image is generated by combining the respective temperature information estimated from the IR targetsignature and background scene. Finally, the synthesized temperature image is transformed into an IR radiance image of threeIR-WBs. Through the proposed method, various IR synthesis experiments are performed for various IR target signature andbackground scenes.

1. Introduction

Due to easy investigation and cost reduction in principleestablishment and performance evaluation for functionalityof an IR system, the demand for IR simulators is quicklyincreasing for military purpose research. Recent military IRsimulators such as IR countermeasures (IRCM), directedIRCM (DIRCM), and IR reticle seeker are broadly used forevaluation, efficiency, and modification for actual militaryIR sensor systems [1–8]. The basic materials for analyzingarbitrary IR system are IR target and background images

which are transformed as IR signatures with an intensity(gray-level) distribution corresponding to the temperaturerange of the IR target and background scene. Based on vari-ous theories such as heat transfer theory, atmospheric trans-fer characteristics, and radiation principle under materialproperties and atmospheric environment, various IR signa-ture modeling methods have been studied [9–13]. And com-mercial IR signature modeling tools such as OKTAL-SE [14],Vega Prime Sensor [15], and MuSES [14] were developed forpredicting IR signatures and simulating virtual reality by IR-WBs. However, those IR signature modeling tools require a

HindawiJournal of SensorsVolume 2019, Article ID 9423976, 17 pageshttps://doi.org/10.1155/2019/9423976

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number of settings for different substances and atmosphericenvironments under certain circumstances. And those havemany security limitations with high cost. In the researchesfor the IR signature modeling, Pan et al. [9] and Lu andWang[10] analyzed the IR radiance and surface temperature forhelicopters and airplanes, respectively. And Dulski et al.[11] modeled virtual backgrounds as IR signatures foratmospheric environments such as the sky and the clouds.However, the IR signature modeling for IR targets andbackgrounds is very challenging, because backgrounds aregenerally composed of various substances and have com-plex absorption, reflection, and scattering characteristics.

A typical example among IR simulators for military pur-poses is the DIRCM system for protecting friendly forcesfrom an IR-guided (or heat-seeking) missiles as shown inFigure 1(a). In order to actively cope with detected IR-guided missiles, various IRCMs, based on flare or jammerto protect friendly forces against an attack of enemy missiles,have been developed up to now [17–19]. The flare counter-measure is vulnerable to approach attacks and limited in itsamount of loading and continuative launch. The jammercountermeasure is categorized as an omnidirectional anddirected method [19]. Since the omnidirectional jammeremits IR source in all directions, it requires big power useand very high accuracy for directly shooting strong jammingsignals to a missile seeker. In order to improve the vulnerabil-ity of the omnidirectional jammer, the DIRCMs have beenresearched [20]. Because the directed jammer utilizes highluminance lamps (or laser) for converging the jammingenergy upon the missile seeker, it can emit the jammingenergy continuatively without wasting. Hence, it can imme-diately counteract the missile seekers for tracking and attack-ing flying targets such as aircrafts or helicopters. In order touse the jammer efficiently in the DIRCM, it is necessary to

have an IR detection system capable of recognizing missilesor threats. The IR detection system also requires the abilityto recognize objects and backgrounds under various IR-WBs. For that reason, the synthesis of an IR target and back-ground by IR-WBs can be used for enhancing the perfor-mance of the IR detection system.

In the case of surface-to-air IR-guided missiles, an air-plane and the sky become the target and background scene,respectively, in the missile standpoint. In order to detect atarget, the reticle seeker or IR-guided missile [17–19] with asingle detector utilizes SWIR- or MWIR-WB while an imag-ing seeker mainly exploits LWIR-WB [7, 21, 22]. Therefore,the military IR simulations require IR images of the samescene by three IR-WBs and the synthesis technique of an IRtarget and background scene as shown in Figure 1(b). How-ever, it is not easy to produce IR images of three WBs forthe same scene due to lack of equipment. Furthermore, syn-thesizing IR targets and backgrounds is very challengingbecause of the differences in their original WB characteris-tics. Some related studies are as follows. The basic step forthose simulations is IR signature modeling for IR target andbackground. Kim et al. [7] constituted IR models for targetswith an internal heat source and generated thermal imagesproduced by the optical system of the reticle seeker andatmospheric turbulence. And Cox et al. [23] modeled an air-craft, a cloud, a background, and a sensor for generating anIR scene. Bae et al. [24] studied an IR-WB conversion tech-nique for military IR sensor simulation. In this study, real tar-gets as well as modeled targets by RadThermIR [25] wereused and the mentioned IR-WB conversion technique [24]was applied for the synthesis of IR targets and backgrounds.

This paper proposes an IR synthesis method for generat-ing an IR image of three WBs by synthesizing a modeled (oractual) IR target and real IR background image for arbitrary

(a)

A B

C D

(b)

Figure 1: (a) Synthesis example of laser jamming image and (b) example of missile seeker simulation (A: spin-scan seeker, B: cone-scanseeker, C: rosette seeker, and D: imaging seeker).

2 Journal of Sensors

(original) IR-WB. First, the size of an IR target is adjusted to adesired target distance using the distance informationbetween the background image and an image detector. Then,each temperature information is estimated from an IR targetand a real IR background for an original IR-WB, and then asynthesized temperature image is generated by combining,respectively, estimated temperature information. The syn-thesized temperature image is transformed into a radianceimage of desired IR-WB. Finally, the IR image of the desiredIR-WB is produced from the radiance image.

2. Related Works and Assumption

2.1. Radiance Estimation from WB and Temperature. For anobject with temperature T and spectral emissivity ε, the radi-ance for an WB width (λ1~λ2) is given by

L Tð Þ =ðλ2λ1

ε λð ÞC1λ5 exp C2/λTð Þ − 1ð Þ dλ, ð1Þ

where C1 = 1:191 × 104½W μm4/cm2 sr� and C2 = 1:428 ×104½μmK� are the radiation constants [1, 20, 26]. εðλÞ < 1and εðλÞ = 1 are for a gray body and a black body,respectively. Since the temperature to radiance functionof Equation (1) is not easy to estimate the temperatureinversely from the radiance due to the integral operation,it is simplified as follows:

L Tð Þ≐ εC1Δλ

λ5c exp C2/λcTð Þ − 1ð Þ , ð2Þ

where Δλ and λc represent the given WB width and the cen-tral wavelength for the WB range. We assume that the emis-sivity is a constant, namely, the object is a gray body in theWB range. The above simplified formula is easy to estimatethe temperature inversely from the radiance. According tothe atmosphere transmission characteristics, an IR-WBrange is classified into SWIR-WB (1.9~2.9μm), MWIR-WB (3~5μm), and LWIR-WB (8~12μm). The output volt-age from an IR detector for spectral transmittances τatm

(Synthesized IR images)

IR background under arbitrary IR-WB

Temperature estimation

IR target under arbitrary IR-WB

Temperature estimation

Synthesized temperature image

IR image generation for desired IR-WB

LWIR MWIR SWIR

Size set-up

Temperature compensation

Temperature compensation

Temperature to radiance function

Radiance to gray-level transfer function

Radiance estimation

Radiance estimation

Figure 2: Block diagram of the proposed IR synthesis imagegeneration method.

Temperature (K)

200 300 400 500 600

Radi

ance

(W/c

m2 s

r)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

LWIR (integral)MWIR (integral)SWIR (integral)

LWIR (approximated)MWIR (approximated)SWIR (approximated)

(a)

Temperature (K)200 300 400 500 600

Tem

pera

ture

estim

atio

n er

ror (

K)

-5

0

5

10

15

SWIRMWIRLWIR

(b)

Figure 3: (a) Original radiance and approximately estimated oneand (b) error of approximately estimated temperature fromoriginal temperature for three IR-WBs.

TT⁎

T

L⁎(T)

dL⁎(T)/dT

L⁎(T)

L(T)

L(T)

A

Figure 4: The original radiance curve and approximately estimatedone according to arbitrary temperature.

3Journal of Sensors

and τdet for an atmosphere and the IR detector is calculatedas follows:

Vdet = ApΩL Tð Þðλ2λ1

τatm λð Þτdet λð ÞS λð Þdλ, ð3Þ

where S is the spectral responsivity of the IR detector in thegiven WB range. And Ω and Ap represent the solid angle ofthe IR detector from a target and the target area on the IRdetector [27]. If τatm, τdet, and S are WB invariants withconstants Ap and Ω, the IR detector’s output voltage is pro-portional to the target radiance, a function of the target tem-perature. The radiance formula of Equation (1) is a functionof the spectral emissivity. However, for a black body or agray body, the spectral emissivity becomes a constant andthen the radiance formula of Equation (1) just becomes afunction of temperature. In the case of a 2D imaging detec-tor such as the imaging detector of Figure 1(d), the gray levelof an IR image is also proportional to an output voltage of anIR detector. Therefore, the radiance of specific WB for anobject temperature can be regarded as proportional to a graylevel of an IR image.

2.2. Assumption of Transmittance and Emissivity. Manymaterials have different transmissivity, emissivity, and reflec-tivity properties according to the IR-WB. Among atmo-spheric gases, in particular, water vapor and carbon dioxidehave specific emission and absorption characteristics accord-ing to the IR-WB. The LWIR-WB and MWIR-WB detect thethermal radiation emitted from a material itself, while theSWIR-WB uses active SWIR reflectivity of a material.Because of this reason, the IR-WB conversion is a verydemanding research field. In this paper, the IR-WB conver-sion is limited to the conversion from LWIR-WB toMWIR-WB and SWIR-WB, and the IR image synthesis isperformed on military targets composed of metals (e.g., tank,ship, helicopter, and airplane) and monotonous far-fieldbackgrounds (e.g., sky, ground, and sea) that are frequentlyused in IRCM or IRST. Also, to reduce the complexity ofour experiment, some assumptions about object emissivityand atmospheric transmissivity by the IR-WB are describedas follows.

First, our experiments have limitations in the WB con-version related to emissivity of a target that varies with theIR-WB. In fact, many materials are not a gray body withemissivity independent of the IR-WB. In a modern militaryIR imaging system such as IRST or IRCM, the criterion for

(a)

𝜀 = 1.0

𝜀 = 0.8

𝜀 = 0.6

T

L(Tmax)

L(Tmin)

G(L)TminGminGmax Tmax

L(T)

𝛼 (slope)

LWIR imagePhysical temperature

⁎ Using two pixels (typically min, max gray-level)

⁎ Assuming that min and max temperature of an object are

known

⁎ Assuming that emissivity of regions of an object is known

(b)

Figure 5: (a) Modeled Agusta helicopter and (b) relation of radiance and gray level according to object temperature.


dividing the target with a background is metal or nonmetal.The metal emissivity decreases rapidly with increasing IR-wavelength. The metal emissivity sharply decreases fromabout 75% to 25% in the SWIR-WB, then slowly decreasesfrom about 20% to 10% for the MWIR-WB to LWIR-WB.On the contrary, the nonmetal emissivity is about 30%,50~85%, and 90% for the SWIR-WB, MWIR-WB, andLWIR-WB. It can be seen that the emissivity of the metaland nonmetal is much higher in the MWIR- and LWIR-WB compared to the SWIR-WB. So assuming that the target

and background are gray body or black body, theWB conver-sion between the LWIR-WB and MWIR-WB may be morereasonable compared to the WB conversion between theLWIR-WB and SWIR-WB.

Second, our experiments have some WB conversion con-straints related to transmittance of water vapor and carbondioxide which greatly affects atmospheric transmittance.The IR-WB is affected by the concentration of water vaporand carbon dioxide. The concentration of these two materialsis, respectively, about 3 × 10-4 ppm for carbon dioxide and 0

LWIR

Temperature, T⁎ (K)300 350 400 450 500

Estim

ated

tem

pera

ture

(K)

300

350

400

450

500

Approximated, T⌃

Compensated, T~

(a)

MWIR

Temperature, T⁎ (K)300 350 400 450 500

Estim

ated

tem

pera

ture

(K)

300

350

400

450

500

Approximated, T⌃

Compensated, T~

(b)

SWIR

Temperature, T⁎ (K)300 350 400 450 500

Estim

ated

tem

pera

ture

(K)

350

400

450

500

Approximated, T⌃

Compensated, T~

300

(c)

LWIR

Temperature, T⁎ (K)300 350 400 450 500

Tem

pera

ture

erro

r (K)

-2

0

2

4

Approximated, e⌃TCompensated, e~T

(d)

MWIR

Temperature, T⁎ (K)300 350 400 450 500

Tem

pera

ture

erro

r (K)

-2

-4

0

2

4

6

8


(e)

SWIR

Temperature, T⁎ (K)300 350 400 450 500

Tem

pera

ture

erro

r (K)

20

468

10121416


(f)

Figure 6: Approximately estimated temperature and compensated one for (a) LWIR-WB, (b) MWIR-WB, and (c) SWIR-WB; temperatureerror for (d) LWIR-WB, (e) MWIR-WB, and (f) SWIR-WB in 300K~500K.

5Journal of Sensors

Gray-level (GL)

Diff

. of i

nteg

rate

d G

L an

d ap

prox

imat

ed G

L (G

i – G

a)

<L → M>

0

1

2

3

4

5

6

7

0 32 64 96 128 160 192 224

(a)

<L → M>

Diff

. of i

nteg

rate

d G

L an

d co

mpe

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L (G

i – G

c)

Gray-level (GL)

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0 32 64 96 128 160 192 224

(b)

<L → S>

Gray-level (GL)

Diff

. of i

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prox

imat

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L (G

i – G

a)

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<L → S>

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0 32 64 96 128 160 192 224

Diff

. of i

nteg

rate

d G

L an

d co

mpe

nsat

ed G

L (G

i – G

c)

Gray-level (GL)

(d)

Figure 7: Differences of integral gray level and (a) approximately estimated gray level and (b) compensated gray level in LWIR-WB-to-MWIR-WB conversion; differences of integral gray level and (c) approximately estimated gray level and (d) compensated gray level inLWIR-WB-to-SWIR-WB conversion for temperature range of 200K~600K and gray level of 0~255.

Approximately estimated temp. Compensated temp.

SCEN

E1SC

ENE2

Original LWIR Converted MWIR Converted SWIR

350

200350

200

350

200350

200(a) (b) (c) (d) (e)

Figure 8: (a) Original LWIR image, pseudocolor image of (b) approximately estimated temperature and (c) compensated temperature,(d) converted MWIR image, and (e) converted SWIR image for SCENE1 and SCENE2.


to 8 × 10-3 ppm for water vapor from troposphere to ground(about 11 km or less). Because IR images used in an IRST orIRCMsimulator areusually taken fordistances less than10 kmfrom the viewing position, it can be assumed that the concen-tration of these two materials is constant. The transmittanceof atmospheric gases including water vapor and carbon diox-ide is nearly closed to 1 in the MWIR-WB and LWIR-WB,whereas it is uneven in the SWIR-WB. So assuming that theatmospheric transmissivity of Equation (3) is uniform, theWB conversion between the LWIR-WB and MWIR-WBmay be more reasonable compared to the WB conversionbetween the LWIR-WB and SWIR-WB, similar to the rela-tion between emissivity andWB conversion described above.

3. Proposed IR Composite Image GenerationMethod Using Temperature Synthesis

This paper proposes an IR composite image generationmethod for creating an (synthesized) IR image of threeWBs (LWIR, MWIR, and SWIR) by synthesizing a modeled(or actual) IR target image and an IR background image ofan arbitrary IR-WB as shown in Figure 2. For a given IR tar-get image and IR background image of certain WB, min andmax temperatures are assigned to min and max gray levels.Also, the radiance intensities corresponding to the min andmax gray level are obtained using Equation (2). The radianceintensities corresponding to the gray levels between the minand max gray levels can be obtained based on the propor-tional relationship of the radiance intensity and gray level,and then temperatures corresponding to the obtained radi-

ance intensities for the gray levels between min and maxgray levels are estimated using Equation (6). Then, theapproximately estimated temperature is compensated byusing the slope information of original temperature-radiance curve and the approximated one. Compensatedtemperature images estimated from the IR target imageand the IR background image are synthesized together.Additionally, in order to synthesize the IR target tempera-ture image in the IR background temperature image, theIR target size may be adjusted in consideration of a desiredtarget distance between the IR background and an imagedetector [5]. The synthesized temperature image is con-verted into a radiance image using Equation (2); then thesynthesized IR image is finally generated by the radiance togray-level transfer function.

3.1. Size Settings of IR Target. First, the size of a modeled(actual) IR target to be synthesized in an IR backgroundimage should be set according to an actual target distance Dbetween the target and an image detector. When the horizon-tal and vertical fields of view (FOV) of the image detector aregiven in the relationship between the target position and thevisibility width for the FOV of the image detector, FOVh andFOVv from the target distance can be calculated as follows:

FOVh = θ = tan−1 Wb h

D,

FOVv = θ = tan−1 Wb v

D,

ð4Þ

Sky Ground Sea

Modeled targets

Real targets

Real backgrounds

mF16mAgusta-helicopter

mTANK

mSHIP1mSHIP2

F35

BOEING

TANK1

TANK2

SHIP1

SHIP2

SKY1

SKY2

SKY3

GROUND1

GROUND2

SEA1

SEA2

Figure 9: Modeled and actual targets and background images for sky, ground, and sea.

7Journal of Sensors

whereWb h andWb v mean the horizontal and vertical back-ground visibility width in the actual background environ-ment. In the case of the visibility width of the backgroundimage, Wb h′ and Wb v′ at a target distance D′, the actual tar-get size is constant; however, the target on the obtained back-ground image is largely shown comparatively. For theobtained background image size Xb × Yb, the backgroundvisibility is Wb h ×Wb v. Therefore, if the actual target sizeWt h ×Wt v is known, the target size Xt and Yt on an imageis given as follows:

Xt = XbWt h

Wb h,

Yt = YbWt v

Wb v:

ð5Þ

3.2. Temperature Estimation from Radiance Intensity. Whenthere is radiance intensity information per pixel for an IRtarget image and background image, the temperature corre-

sponding to the radiance of individual pixels can be esti-mated using the inverse function of Equation (2). So therelation formula for calculating temperature T from radianceL can be derived as follows:

T Lð Þ≐C2λc

ln εC1Δλ

Lλ5c+ 1

� �−1: ð6Þ

Since the IR-WB transformation method using a temper-ature of an object uses Equation (2) approximating Equation(1) including the integral operator, it is easy to estimate thetemperature from the radiance through Equation (2), butthe approximately estimated temperature contains someerror due to the simplification of the equation. Figure 3(a)shows the original radiance obtained by Equation (1) andapproximately estimated one calculated by Equation (2)in 200K~600K temperature range for three IR-WBs.Figure 3(b) shows the error of approximately estimated tem-perature from the original temperature for three IR-WBs.

Approximated temp. LWIR MWIR

(a)

SWIR

260K

370K

260K

370K

260K

370K

260K

370K

260K

370K

260K

370K

Information

(b)

(c)

(d)

(e)

(f)

FOVH = 10°, FOV

V = 8°, Tt_min = 270, Tt_max = 370, Tb_min = 260, Tb_min = 320,

D = 0.5 Km

FOVH = 10°, FOV


D = 0.3 Km

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV


D = 0.3 Km

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV


D = 0.3 Km

SKY3

SKY2

SKY1

Compensated temp.

Figure 10: Resulting images from synthesizing modeled sky target (mF16) and sky background images for three IR-WBs.


The temperature estimation error means the differencebetween the original temperature and the approximately esti-mated one. We can know that the radiance intensity for thetemperature and the temperature estimation error have dif-ferent characteristics for each WB.

Figure 4 shows the original radiance curve L∗ðTÞ andapproximately estimated oneLðTÞ according to arbitrary tem-perature. T represents the estimated temperature obtainedfrom the radiance LðTÞ using Equation (6). The compensatedtemperature ~T obtained using only the first order tangentslope of L∗ðTÞ for T is as follows:

~T = T − 2 ⋅ ΔT ⋅ e T� �

⋅ L∗ T + ΔT� �

− L∗ T − ΔT� �� −1, ð7Þ

where eðTÞ = L∗ðTÞ − LðTÞ. And L∗ðTÞ is the original radi-ance for the approximately estimated temperature, and ΔTrepresents the microtemperature displacement value toobtain the tangential slope at the A point in the figure.

3.3. Relationship of Radiance and Gray Level according toTemperature. Figure 5(a) shows the meshing result for theAgusta helicopter model made by RadThermIR. Figure 5(b)shows the transformation relation between radiance and graylevel according to the target temperature. In case the object isa gray-body (i.e., emissivity is constant), the function shapeof the radiance intensity is similar with that of a black body;however, the height of the function is inversely proportionalto the object emissivity. Figure 5(b) shows an example thatthe pixel with themax gray levelGmax corresponds to the win-dow with max temperature Tmax (ε = 0:8) and the pixel withthe min gray level Gmin represents the interval engine withmin temperature Tmin (ε = 0:6) in the acquired IR image.

The gray-level transfer function GðLÞ using the radiancecan be calculated as follows:

G Lð Þ = a L − L Tminð Þ½ � +Gmin, ð8Þ

where a (= ðGmax −GminÞ/ðLðTmaxÞ − LðTminÞÞ) is the slope ofthe transfer function. The radiances, LðTminÞ and LðTmaxÞ for


(a)

SWIR

260K

360K

260K

360K

260K

360K

260K

360K

260K

360K

260K

360K

Information

(b)

(c)

(d)

(e)

(f)

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV

V = 8°, Tt_min = 270, Tt_max = 360, Tt_min = 260, Tt_min = 320,

D = 0.3 Km

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV


D = 0.5 Km

FOVH = 10°, FOV


D = 0.3 Km

SKY3

SKY2

SKY1

Compensated temp.

Figure 11: Resulting images from synthesizing modeled sky target (mAgusta-helicopter) and sky background images for three IR-WBs.

9Journal of Sensors

the temperatures, Tmin and Tmax corresponding to the minand max gray level, Gmin and Gmax are obtained by Equation(2). Using Equation (8), the radiance LðGÞ from the gray levelG can be inversely calculated by

L Gð Þ = G −Gmina

+ L Tminð Þ: ð9Þ

This mentioned radiance estimation method usingtemperature and gray level assumes that the temperatureand emissivity of objects corresponding to at least twopixels in an IR image are known. Larger pixel values andtemperature differences for the two pixels ensure betterradiance estimates.

3.4. Proposed IR Synthesis Image Generation Method Basedon Temperature Synthesis. In this step, it is assumed that anIR target image size to be synthesized in an IR backgroundimage was adjusted according to a target distance as men-tioned in Section 3.1. The total procedure of the proposedIR synthesis image generation method using temperaturesynthesis is as follows.

3.4.1. Step I: Min and Max Temperature Assignment to Minand Max Gray Level for IR Target and Background. In anoriginal IR target (or background) image of arbitrary IR-WB to be converted, when assuming that min and max tem-peratures Tt:min and Tt:max (or Tb:min and Tb:max for the back-ground image) are assigned to min and max gray levels Gt:minand Gt:max (or Gb:min and Gb:max for the background image),the radiance intensities LðTt:minÞ and LðTt:maxÞ (or LðTb:minÞand LðTb:maxÞ for the background image) corresponding to

the gray levels or the temperatures are calculated throughEquation (2).

3.4.2. Step II: Temperature Assignment to All RemainingPixels for IR Target and Background. The target (or back-ground) radiance intensity LðGtÞ (or LðGbÞ) for gray levelGt (or Gb) of all remaining pixels in the IR target (or back-ground) is obtained using Equation (9). And the approxi-mately estimated target (or background) temperature Tt (orTb) corresponding to the obtained radiance intensity is cal-culated through Equation (6); then the compensated target(or background) temperature ~Tt (or ~Tb) is calculated throughEquation (7).

3.4.3. Step III: Synthesized Temperature Image with TargetEmbedded in Background Image. As the compensated targettemperature image ~Tt is synthesized at a desired location inthe compensated background temperature image ~Tb withthe desired size, the synthesized temperature image ~Ts withthe target embedded in the background is created. The sub-script smeans the synthesis of the IR target and background.This step assumes that the target image was scaled as intro-duced in Section 3.1.

3.4.4. Step IV: Radiance Intensity of Desired IR-WB forSynthesized Temperature. For the synthesized temperatureimage ~Ts composed of the IR target and background, theradiance intensity Ls of the desired IR-WB is calculated usingEquation (2) with the emissivity. ε = 0:9 is experimentallyused based on the assumption in Section. 2.2.


(a)

SWIR

270K

360K

270K

360K

270K

360K

270K

360K

Information

(b)

(c)

(d)

FOVH = 10°, FOV


D = 0.6 Km

FOVH = 10°, FOV


D = 0.8 Km

FOVH = 5°, FOV

V = 3.3°, Tt_min = 270, Tt_max = 360, Tt_min = 260, Tt_min = 330,

D = 0.2 Km

FOVH = 5°, FOV


D = 0.1 Km

SEA1

SEA2

GROUND1

GROUND2

Compensated temp.

Figure 12: Resulting images from synthesizing modeled ground, sea target (mTANK and mSHIP1, mSHIP2) and ground, and seabackground images for three IR-WBs.


3.4.5. Step V: Generation of Desired IR-WB Image forRadiance Intensity. Finally, the desired IR-WB image is gen-erated by the radiance-to-gray-level transfer function GðLsÞusing the radiance intensity Ls of the desired IR-WB obtainedin the step III for the synthesized temperature image. Theradiance-to-gray-level transfer function is given by

G Lsð Þ = α255 Ls − Ls:minð ÞLs:max − Ls:min

+ β, 0 ≤ G Lsð Þ ≤ 255, ð10Þ

where α ð0 < α ≤ 1Þ and β mean the contrast and brightnesscontrolling constant. For α = 1 and β = 0, the gray level ofthe desired IR-WB image is distributed at 0~255.

4. Results and Discussion

4.1. Verification of Temperature Estimation. Figure 6 showsthe approximately estimated temperature T , the compen-sated temperature ~T , and their temperature error with theoriginal temperature T∗ (given by Equation (1)) for the threeIR-WBs. The temperature error represents the difference

between the original temperature and the approximately esti-mated one or the compensated one. The errors of the approx-imately estimated temperature and the compensated one aregiven by eT = T − T∗ and ~eT = ~T − T∗, respectively. In theLWIR-WB of Figures 6(a) and 6(d), the sign of the approxi-mately estimated temperature error changes from negative topositive at 410K, and the compensated temperature errordecreases significantly over all temperature ranges. In theMWIR-WB of Figures 6(b) and 6(e), the sign of the approx-imately estimated temperature error changes from positive tonegative at 420K, and it can be seen that after temperaturecompensation, the error is greatly reduced, similar toLWIR-WB. In the SWIR-WB of Figures 6(c) and 6(f), theapproximately estimated temperature is higher than thecompensated temperature from 300K to 500K. The errorsfor these two temperatures decreased with increasing tem-perature. And the error of the compensated temperaturedecreased significantly than that of the approximately esti-mated temperature. And the compensated temperature alsocaused an error of 4.5K at 300K.

Figure 7 shows the comparison of the gray levels calcu-lated from the approximately estimated temperature and


(a)

SWIR

260K

500K

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Information

(b)

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FOVH = 10°, FOV


D = 0.5 Km

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D = 0.3 Km

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D = 0.3 Km

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D = 0.5 Km

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D = 0.3 Km

SKY3

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Compensated temp.

Figure 13: Resulting images from synthesizing the real sky target (F35) and sky background images for three IR-WBs.

11Journal of Sensors

the compensated temperature. The integral gray level, theapproximately estimated gray level, and the compensatedgray level represent the gray level obtained by the originaltemperature, the approximately estimated temperature, andthe compensated temperature, respectively. The figure showsthe differences of the integral gray level and the approxi-mately estimated gray level and the compensated gray levelin LWIR-WB-to-MWIR-WB and LWIR-WB-to-SWIR-WBconversion. The difference sums for the approximately esti-mated gray level are, respectively, 699 and 1041 in the levelin the converted MWIR-WB and SWIR-WB. On the otherhand, the difference sums for the compensated gray levelare, respectively, 11 and 14 in the level in the convertedMWIR-WB and SWIR-WB. We can know that the compen-sated temperature produces less gray-level difference thanthe approximately estimated temperature.

The IR-WB conversion simulations using the compen-sated temperature were performed. The IR images (SCENE1and SCENE2) used in the simulation were acquired byTAU640, a LWIR camera of FLIR Corp., as shown inFigure 8(a). In the test images, the ground structure has thebrightest gray level (pixel value of 255). On the other hand,

the sky background region has the lowest gray level (pixelvalue of 0). These temperatures measured using an IR ther-mometer were 320K and 270K, respectively. Figures 8(b)and 8(c) show the pseudocolor images of the approximatelyestimated temperatures and the compensated temperatureextracted from the test LWIR images. In the LWIR-WB ofFigure 6(a), it can be seen that the approximately estimatedtemperature is lower than the compensated temperature bel-low 400K so approximately estimated temperature is bluerthan the compensated temperature in the pseudocolor asshown in the black box of the figure. As shown in the whitebox in the figure, the compensated temperature below400K rises above the approximately estimated temperatureand becomes redder. Figures 8(d) and 8(e) show the WB-converted MWIR and SWIR images using the compensatedtemperatures.

The IR-WB conversion is mainly performed from LWIR-WB to MWIR-WB or SWIR-WB. The LWIR-image is usedmore widely than other WB images, and its gray-level distri-bution according to temperature is relatively linear thanthose of other IR-WBs. On the other hand, SWIR imagehas little brightness change in the low temperature region


(a)

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260K

380K

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Information

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FOVH = 10°, FOV


D = 1 Km

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D = 0.5 Km

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D = 1 Km

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D = 0.5 Km

FOVH = 10°, FOV


D = 1 Km

FOVH = 10°, FOV


D = 0.5 Km

SKY3

SKY2

SKY1

Compensated temp.

Figure 14: Resulting images from synthesizing real sky target (BOEING) and sky background images for three IR-WBs.


because the radiance is very small in that region. This meansthat the IR-WB conversion using SWIR-WB may not beeffective. Also, as shown in Figure 6(e), because the compen-sated temperature for MWIR-WB has a very small error onlybelow 350K, the IR-WB conversion using the MWIR-imageis more reasonable than that using the SWIR-image.

4.2. Preparation of IR Target and Background Images forImage Synthesis. For the verification of the proposed IRcomposite image method, we used modeled or actual LWIRtarget images consisting of three groups of IR target images(sky targets, ground targets, and sea targets) as shown inFigure 9. The LWIR images for the modeled targets (mF16,mAgusta-helicopter, mTANK, mSHIP1, and mSHIP2) wereproduced from the respective 3D CAD models by RadTher-mIR [25] and MuSES [16], IR signature modeling tools ofTAI corporation. The targets composed of mesh (or lattice)were modeled, and the radiance per each lattice was calcu-lated through thermal analysis of the modeling tool. Thenthe LWIR target images were created as shown in the figure.We also used the actual LWIR target images (F35, BOEING,TANK1, TANK2, SHIP1, and SHIP2) actually photographedby thermal equipment. Similar to the LWIR target images,the actual LWIR background images are composed of threegroups: sky backgrounds (SKY1 with high contrast andSKY2 and SKY3 with low contrast), ground backgrounds(GROUND1, GROUND2 with low contrast), and sea back-grounds (SEA1, SEA2 with high contrast). The FOVs of theseIR background images were 10° × 8° for the sky and sea back-grounds and 5° × 3:3° for the ground backgrounds. If it isassumed that all objects in the prepared target and back-ground images are the gray body, the temperature informa-

tion of the target images and the background images canbe, respectively, estimated by the proposed procedure. Aftergenerating one temperature synthesis image synthesizing atarget temperature image and a background temperatureimage, an IR synthesis image of desired IR-WB is generated.

4.3. Synthesis Experiments for IR Target and Background.Figures 10 and 11 show the resulting images from synthesiz-ing the modeled sky targets (mF16 and mAgusta-helicopter)and the real sky background images (SKY1, SKY2, andSKY3) for the three IR-WBs. For a variety of temperaturedistributions, FOVs, and target distances, various IR synthe-sis images were created for the three WBs based on thecompensated temperature and the compensation of theapproximately estimated temperature. In the temperatureextraction from the modeled targets and the LWIR back-ground images, it can be seen that the compensated temper-ature rises slightly higher than the approximately estimatedtemperature in the pseudocolor. The MWIR and SWIRimages were, respectively, transformed using the synthesizedtemperature images estimated from the LWIR images of themodeled targets and the real sky background images. In theconversion of high-WB to low-WB for the mF16 andmAgusta-helicopter model, the body region in the low tem-perature region became darker due to reduction of radianceintensity, while the engine region in the high temperatureregion became brighter by the increase of radiance intensity.In the conversion of high-WB to low-WB for the sky back-ground images, the high temperature regions are highlightedby radiance intensity increased by the conversion function,for example, the structures near the road in SKY1, the smallstructures on the mountain in SKY2, and the partial cloud


(a)

SWIR

270K

360K

270K

360K

270K

360K

270K

360K

Information

(b)

(c)

(d)

FOVH = 10°, FOV


D = 0.7 Km

FOVH = 10°, FOV


D = 0.8 Km

FOVH = 5°, FOV


D = 0.1 Km

FOVH = 5°, FOV


D = 0.1 Km

SEA1

SEA2

GROUND1

GROUND2

Compensated temp.

Figure 15: Resulting images from synthesizing modeled ground, sea target (TANK1, TANK2, SHIP1, and SHIP2) and ground, and seabackground images for three IR-WBs.


regions above in SKY3. On the other hand, lower tempera-ture regions are darkened due to the decrease in radianceintensity, for example, the upper cloud region and low hillregion in SKY1, the upper cloud region in SKY2, and thelower cloud region in SKY3.

Figure 12 shows the resulting images from synthesizingthe modeled ground target (mTANK) and the modeled seatargets (mSHIP1 andmSHIP2) with the ground backgrounds(GROUND1 and GROUND2) and the real sea backgrounds(SEA1 and SEA2) for three IR-WBs. Similar to the conver-sion for the modeled sky targets, the body region of the mod-eled tank and ship, which have low temperature, is darkenedon the whole, while the moving rotational axis and caterpillarregion of mTANK with high temperature, the upper regionof mSHIP1 by direct sunlight, are highlighted. GROUDN1and GROUND2 have somewhat wide temperature rangeand gray-level distribution; on the other hand, SEA2 andSEA3 have narrow ones. In the converted MWIR and SWIRimages, the high temperature regions are highlighted by radi-

ance increased by the conversion function, for example, thestructures near the road in GROUDN1 and GROUND2and the partial ground regions in SEA1 and SEA2. On theother hand, low temperature regions are darkened due tothe decrease in radiance intensity, for example, the mountainand the hill region in GROUDN1 and GROUND2 and thesea region in SEA1 and SEA2.We can see that the pixel valuesof the high temperature region expand due to the increase ofradiance, whereas the pixels of low temperature regiondecrease due to the decrease of radiance (i.e., expansion andcompression effect of pixel values by the conversion functionwith exponential shape) in the brightness of the convertedMWIR and SWIR images. In the case of the SWIR andMWIR synthesis images, it is difficult to distinguish the back-ground because the radiance at low temperatures is very low.On the other hand, in the case of the LWIR synthesis images,it is easier to distinguish even objects with low temperature.

Figures 13, 14, and 15 show the synthesis result for eachIR-WB, applying the actual sky targets (F35 and BOEING),

100 200 300 400 500 600Horizontal pixels

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LWIR for target lineMWIR for target lineSWIR for target lineLWIR for background lineMWIR for background lineSWIR for background line

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Figure 16: Pixel values of the target line and background line in (a) Figure 10(a), (b) Figure 11(c), (c) Figure 12(a), and (d) Figure 12(c)including modeled targets.


the ground targets (TANK1 and TANK2), and the sea targets(SHIP1 and SHIP2) for the same sky, ground, and sea back-grounds. We can see that the IR-WB conversion results usingthe actual targets are similar to those obtained by applyingthe modeled targets.

Figures 16 and 17 show the pixel value of the red, green,and blue lines (solid line for target line and dot line for back-ground line) on the converted LWIR, MWIR, and SWIRimage. Figures 16(a)–16(d) are, respectively, correspondingto the result of Figures 10(a), 11(c), 12(a), and 12(c) includ-ing the modeled targets, and Figures 17(a)–17(d) are, respec-tively, corresponding to the result of Figures 13(e), 14(e),15(b), and 15(d) including actual targets. In the case ofFigure 16 applying the modeled targets, it can be seen thatthe pixels of the targets were made of almost metal and thebackgrounds decrease proportionally from high-WB tolow-WB as a whole. However, in Figures 16(a) and 16(b),since the pixel values of the engine part of mF16 andmAgusta-helicopter are very high, it can be seen that there

is almost no change in the pixel values even if theWB conver-sion is performed. In addition, the pixel values of the normalbackgrounds of Figures 16(b)–16(d) are monotonouslydecreased at the WB conversion, whereas very low pixelvalues of the dark clouds in Figure 16(a) are not decreasedproportionally due to very low pixel values. The mentionedfeatures are similar in Figure 17 applying the modeled tar-gets. It can be seen that the pixel values corresponding tothe actual targets and backgrounds also decrease proportion-ally at the WB conversion. This is because the almost targetsare mostly composed of metals and the backgrounds in theIR system are relatively monotonous, so the pixel value ofsuch a region exists between 0 and 255, in spite of the factthat emissivity changes for specific objects in the IR-WBsare irregular in general. We also can see that saturation phe-nomenon of pixel value occurs even if the IR-WB is changed,because the pixel value is very high in the plume region ofF35 in Figure 17(a), the engine region of BOEING inFigure 17(b), and the caterpillar region of the tank in

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Figure 17: Pixel values of the target line and background line in (a) Figure 13(e), (b) Figure 14(e), (c) Figure 15(b), and (d) Figure 15(e)including actual targets.


Figure 17(c). We confirmed that the IR background imagescan be synthesized with modeled or actual targets by settingtemperature of the target and background, target distance,and FOV arbitrarily. To make a more precise synthesis, itneeds information about an IR camera for IR backgroundphotographing and climatic conditions during an imageacquisition. In particular, environmental information of IRphotograph, such as time, longitude, weather, temperature,humidity, and altitude at the photograph location, can beused for more exact temperature estimation of an object.

5. Conclusions

This paper proposes an IR composite image generationmethod for modeled (or actual) IR targets and real IR back-grounds. The synthesized temperature image is generatedby combining, respectively, estimated temperature informa-tion from IR targets and IR backgrounds. The gray level onWB-converted images for real IR backgrounds and IR targetsis adjusted based on their estimated temperature and radi-ance information. For the actual test, we synthesized variousmodeled targets and real backgrounds. We confirmed that IRbackground images can be synthesized with IR targets withsetting temperature of the target and background, target dis-tance, and FOV arbitrarily. The IR composite image can beused for the simulation of image seekers for IR-guided mis-siles. In case the IR composite images are used by overlappingthem on IR reticle patterns for DIRCM, they can also beapplied to an IR reticle seeker simulation.

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interestregarding the publication of this paper.

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