Understanding Infrared Thermal Testing Reading 2

Infrared Thermal Testing 

Reading II 

My ASNT Level III 

Pre-Exam Preparatory 

Self Study Notes 

26th April 2015 

Charlie Chong/ Fion Zhang

Infrared Thermography 


Infrared Thermography 



Fion Zhang at Shanghai 

27th May 2015 

http://meilishouxihu.blog.163.com/ 



Charlie Chong/ Fion Zhang 

http://greekhouseoffonts.com/

Greek letter 


IVONA TTS Capable. 


http://www.naturalreaders.com/

Reading 1 

The Ultimate Infrared Handbook for R&D Professionals 

Contents 

1. IR Thermography – How It Works 

2. IR Detectors For Thermographic Imaging 

3. Getting The Most From Your IR Camera 

4. Filters Extend IR Camera Usefulness 

5. Ultra High-Speed Thermography 


http://www.flirmedia.com/MMC/THG/Brochures/T559243/T559243_EN.pdf

Chapter 1: IR Thermography - How It Works 

IR Thermography Cameras 

Although infrared radiation (IR) is not detectable by the human eye, an IR camera can 

convert it to a visual image that depicts thermal variations across an object or scene. 

IR covers a portion of the electromagnetic spectrum from approximately 900 to 14,000 

nanometers (0.9 μm–14 μm). IR is emitted by all objects at temperatures above 

absolute zero, and the amount of radiation increases with temperature. Thermography 

is a type of imaging that is accomplished with an IR camera calibrated to display 

temperature values across an object or scene. Therefore, thermography allows one to 

make non-contact measurements of an object’s temperature. IR camera construction 

is similar to a digital video camera, see Figure 1.1. 

Figure 1.1. Simplified block diagram of an IR camera 



The main components are a lens that focuses IR onto a detector, plus 

electronics and software for processing and displaying the signals and 

images. Instead of a charge coupled device that video and digital still 

cameras use, the IR camera detector is a focal plane array (FPA) of 

micrometer size pixels made of various materials sensitive to IR wavelengths. 

FPA resolution can range from about 160 × 120 pixels up to 1024 × 1024 

pixels. Certain IR cameras have built-in software that allows the user to focus 

on specific areas of the FPA and calculate the temperature. Other systems 

utilized a computer or data system with specialized software that provides 

temperature analysis. Both methods can supply temperature analysis with 

better than ±1°C precision. FPA detector technologies are broken down 

into two categories: thermal detectors and quantum detectors. A common 

type of thermal detector is an uncooled microbolometer made of a metal or 

semiconductor material. These typically have lower cost and a broader IR 

spectral response than quantum detectors. 

Keywords: 

FPA detector technologies are broken down into two categories: thermal 

detectors and quantum detectors. 



Still, microbolometers react to incident radiant energy and are much lower 

and less sensitive than quantum detectors. Quantum detectors are made 

from materials such as InSb, InGaAs, PtSi, HgCdTe (MCT), and layered 

GaAs/AlGaAs for QWIP (Quantum Well Infrared Photon) detectors. The 

operation of a quantum detector is based on the change of state of electrons 

in a crystal structure reacting to incident photons. These detectors are 

generally faster and more sensitive than thermal detectors. However, they 

require cooling, sometimes down to cryogenic temperatures using liquid 

nitrogen or a small Stirling cycle refrigerator unit. 

Ga 

In 

Si 

As 

Sb 

Hg 

Cd 

Te 

Al 

Gallium 

Indium 

Silicon 

Arsenic 

Antimony 

Mercury 

Cadmium 

Tellurium 

Aluminum 



IR Spectrum Considerations 

Typically, IR cameras are designed and calibrated for a specific range of the 

IR spectrum. This means that the optics and detector materials must be 

selected for the desired range. Figure 1.2 illustrates the spectral response 

regions for various detector materials. Because IR has the same properties 

as visible light regarding reflection, refraction, and transmission, the optics for 

thermal cameras are designed in a fashion similar to those of a visual 

wavelength camera. However, the types of glass used in optics for visible 

light cameras cannot be used for optics in an infrared camera, as they do not 

transmit IR wavelengths well enough. Conversely, materials that are 

transparent to IR are often opaque to visible light. IR camera lenses typically 

use silicon (Si) and germanium (Ge) materials. Normally Si is used for MWIR 

(medium wavelength IR) camera systems, whereas Ge is used in LW (long 

wavelength) cameras. Si and Ge have good mechanical properties, i.e., they 

do not break easily, they are nonhygroscopic, and they can be formed into 

lenses with modern turning methods. As in visible light cameras, IR camera 

lenses have antireflective coatings. With proper design, IR camera lenses can 

transmit close to 100% of incident radiation. 



Figure 1.2. Examples of detector materials and their spectral responses 

relative to IR mid wave (MW) and long wave (LW) bands 

InSb, InGaAs, PtSi, HgCdTe (MCT), and layered GaAs/AlGaAs for QWIP 

(Quantum Well Infrared Photon) detectors. 



Thermal Radiation Principles 

The intensity of the emitted energy from an object varies with temperature and 

radiation wavelength. If the object is colder than about 500°C, emitted radiation lies 

completely within IR wavelengths. In addition to emitting radiation, an object reacts to 

incident radiation from its surroundings by absorbing and reflecting a portion of it, or 

allowing some of it to pass through (as through a lens). From this physical principle, 

the Total Radiation Law is derived, which can be stated with the following formula: 

W = αW + ρW +τW, 

which can be simplified to: 1 = α+ ρ +τ , 

The coefficients a, r, and t describe the object’s incident energy absorbtion alpha (α), 

reflection Rho (ρ), and transmission Tau (τ ). Each coefficient can have a value from 

zero to one, depending on how well an object absorbs, reflects, or transmits incident 

radiation. For example, if ρ = 0, τ = 0, and α = 1, then there is no reflected or 

transmitted radiation, and 100% of incident radiation is absorbed. This is called a 

perfect blackbody. In the real world there are no objects that are perfect absorbers, 

reflectors, or transmitters, although some may come very close to one of these 

properties. Nonetheless, the concept of a perfect blackbody is very important in the 

science of thermography, because it is the foundation for relating IR radiation to an 

object’s temperature. 



Keywords: 

Object’s incident energy: 

• absorbtion alpha (α), 

• reflection Rho (ρ), 

• transmission Tau (τ ), 

• emissivity epsilon (Ɛ). 



Fundamentally, a perfect blackbody is a perfect absorber and emitter of 

radiant energy. This concept is stated mathematical as Kirchhoff’s Law. The 

radiative properties of a body are denoted by the symbol Ɛ, the emittance or 

emissivity of the body. Kirchhoff’s law states that α = Ɛ, and since both values 

vary with the radiation wavelength, the formula can take the form α(λ) = Ɛ(λ), 

where λ denotes the wavelength. 

The total radiation law can thus take the mathematical form 1 = Ɛ + ρ + τ, 

which for an opaque body (τ = 0) can be simplified to 1 = Ɛ + ρ or ρ = 1 – Ɛ 

(i.e., reflection = 1 – emissivity). Since a perfect blackbody is a perfect 

absorber, ρ = 0 and Ɛ = 1. The radiative properties of a perfect blackbody can 

also be described mathematically by Planck’s Law. Since this has a complex 

mathematical formula, and is a function of temperature and radiation 

wavelength, a blackbody’s radiative properties are usually shown as a series 

of curves (Figure 1.3). 



Figure 1.3. Illustration of Planck’s Law 

W α T 4 



Wien’s displacement law 

These curves show the radiation per wavelength unit and area unit, called the 

spectral radiant emittance of the blackbody. The higher the temperature, the 

more intense the emitted radiation. However, each emittance curve has a 

distinct maximum value at a certain wavelength. This maximum can be 

calculated from Wien’s displacement law, 

λ max = 2898/T, or (λ max x T = 2.898 x 10 -3 m.K) 

where T is the absolute temperature of the blackbody, measured in Kelvin (K), 

and λ max is the wavelength at the maximum intensity (in μm). Using blackbody 

emittance curves, one can find that an object at 30°C has a maximum near 

10μm, whereas an object at 1000°C has a radiant intensity with a maximum 

of near 2.3μm. The latter has a maximum spectral radiant emittance about 

1,400 times higher than a blackbody at 30°C, with a considerable portion of 

the radiation in the visible spectrum. 


http://www.flirmedia.com/MMC/THG/Brochures/T559243/T559243_EN.pdfc

Wien’s displacement law 

These curves show the radiation per wavelength unit and area unit, called the 

spectral radiant emittance of the blackbody. The higher the temperature, the 

more intense the emitted radiation. However, each emittance curve has a 

distinct maximum value at a certain wavelength. This maximum can be 

calculated from Wien’s displacement law, 

λ max = 2898/T, or (λ max x T = 2.898 x 10 -3 m.K) 

where T is the absolute temperature of the blackbody, measured in Kelvin (K), 

and λ max is the wavelength at the maximum intensity (in μm). Using blackbody 

emittance curves, one can find that an object at 30°C has a maximum near 

10μm, whereas an object at 1000°C has a radiant intensity with a maximum 

of near 2.3μm. The latter has a maximum spectral radiant emittance about 

1,400 times higher than a blackbody at 30°C, with a considerable portion of 

the radiation in the visible spectrum. 

λ max in meter, T in Kelvin. 


http://hyperphysics.phy-astr.gsu.edu/hbase/wien.html

Wien's Displacement Law 

When the temperature of a blackbody radiator increases, the overall radiated energy 

increases and the peak of the radiation curve moves to shorter wavelengths. When 

the maximum is evaluated from the Planck radiation formula, the product of the peak 

wavelength and the temperature is found to be a constant. 


http://hyperphysics.phy-astr.gsu.edu/hbase/wien.html#c3

This relationship is called Wien's displacement law and is useful for the 

determining the temperatures of hot radiant objects such as stars, and indeed 

for a determination of the temperature of any radiant object whose 

temperature is far above that of its surroundings. 

It should be noted that the peak of the radiation curve in the Wien relationship 

is the peak only because the intensity is plotted as a function of wavelength. If 

frequency or some other variable is used on the horizontal axis, the peak will 

be at a different wavelength. 



Wien's displacement law states that the black body radiation curve for different 

temperatures peaks at a wavelength inversely proportional to the temperature. The shift of that 

peak is a direct consequence of the Planck radiation law which describes the spectral brightness 

of black body radiation as a function of wavelength at any given temperature. However it had 

been discovered by Wilhelm Wien several years before Max Planck developed that more general 

equation, and describes the entire shift of the spectrum of black body radiation toward shorter 

wavelengths as temperature increases. Formally, Wien's displacement law states that the 

spectral radiance of black body radiation per unit wavelength, peaks at the wavelength λ max 

given by: 

λ max x T = b (2.898 x 10 -3 m.K) 

where T is the absolute temperature in Kelvin. b is a constant of proportionality called Wien's 

displacement constant, equal to 2.8977721(26)×10 −3 m K. If one is considering the peak of black 

body emission per unit frequency or per proportional bandwidth, one must use a different 

proportionality constant. However the form of the law remains the same: the peak wavelength is 

inversely proportional to temperature (or the peak frequency is directly proportional to 

temperature). Wien's displacement law may be referred to as "Wien's law", a term which is also 

used for the Wien approximation. 


http://en.wikipedia.org/wiki/Wien%27s_displacement_law

Stefan-Bolzmann law 

From Planck’s law, the total radiated energy W from a blackbody can be 

calculated. This is expressed by a formula known as the Stefan-Bolzmann 

law: 

W = σ ∙T 4 (W/m 2 ), (here the emissivity Ɛ was assumed =1) 

where σ is the Stefan-Bolzmann’s constant (5.67 × 10 –8 W/m 2 K 4 ). As an 

example, a human being with a normal temperature (about 300 K) will radiate 

about 500W/ m 2 of effective body surface. As a rule of thumb, the effective 

body surface is 1m 2 , and radiates about 0.5kW - a substantial heat loss. The 

equations described in this section provide important relationships between 

emitted radiation and temperature of a perfect blackbody. Since most objects 

of interest to thermographers are not perfect blackbodies, there needs to be 

some way for an IR camera to graph the temperature of a “normal” object. 



Wien's displacement law & Stefan-Bolzmann law: Blackbody Radiation, 

Modern Physics, Quantum Mechanics, and the Oxford Comma | Doc Physics 

■ 

https://www.youtube.com/embed/GgD3Um_f0DQ 


https://www.youtube.com/watch?v=GgD3Um_f0DQ

Wien's displacement law & Stefan-Bolzmann law: Max Planck Solves the 

Ultraviolet Catastrophe for Blackbody Radiation | Doc Physics 

■ 

https://www.youtube.com/embed/H-7f-3OAXm0 


https://www.youtube.com/watch?v=H-7f-3OAXm0

Radiation Curves (Intensity Plot) 

The wavelength of the peak of the blackbody radiation curve decreases in a 

linear fashion ? as the temperature is increased (Wien's displacement law). 

This linear variation is not evident in this kind of plot since the intensity 

increases with the fourth power of the temperature (Stefan- Boltzmann law). 

The nature of the peak wavelength change is made more evident by plotting 

the fourth root of the intensity. 



Radiation Curves (Intensity Plot) 



Summarizing 

How a perfect black body changes with temperatures? 

• The λ max changes in a linear manner ? ( inversely proportionally) 

according to Wien's Displacement Law 

• The total total radiated energy W changes in a fourth power T 4 

proportionally according to Stefan-Bolzmann law 



Wien's displacement law (inverse proportional relation) 

λ max x T = b 

λ max = b∙1/T 



Emissivity Ɛ 

The radiative properties of objects are usually described in relation to a 

perfect blackbody (the perfect emitter). If the emitted energy from a blackbody 

is denoted as W bb , and that of a normal object at the same temperature as 

W obj , then the ratio between these two values describes the emissivity (Ɛ) of 

the object, Ɛ = W obj / W bb . Thus, emissivity is a number between 0 and 1. 

The better the radiative properties of the object, the higher its emissivity. An 

object that has the same emissivity Ɛ for all wavelengths is called a greybody. 

Consequently, for a greybody, Stefan- Bolzmann’s law takes the form: 

W = Ɛ∙σ∙T 4 (W/m 2 ), 

which states that the total emissive power of a greybody is the same as that 

of a blackbody of the same temperature reduced in proportion to the value of 

Ɛ for the object. 

Keywords: 

Greybody 



■ωσμ∙Ωπ∆º≠δ≤>ηθφФρ|β≠Ɛ∠ ʋ λαρτ 


http://www.webexhibits.org/causesofcolor/3.html

Still, most bodies are neither blackbodies nor greybodies. The emissivity 

varies with wavelength. As thermography operates only inside limited spectral 

ranges, in practice it is often possible to treat objects as greybodies. In any 

case, an object having emittance that varies strongly with wavelength is 

called a selective radiator. For example, glass is a very selective radiator, 

behaving almost like a blackbody for certain wavelengths, whereas it is rather 

the opposite for other wavelengths. Atmospheric Influence Between the 

object and the thermal camera is the atmosphere, which tends to attenuate 

radiation due to absorption by gases and scattering by particles. The amount 

of attenuation depends heavily on radiation wavelength. Although the 

atmosphere usually transmits visible light very well, fog, clouds, rain, and 

snow can prevent us from seeing distant objects. The same principle applies 

to infrared radiation. 

Keywords: 

Blackbody 

Greybody 

selective radiator 



For thermographic measurement we must use the so-called atmospheric 

windows. As can be seen from Figure 1.4, they can be found between 2 - 

5μm, the mid-wave windows, and 7.5μm – 13.5μm, the long-wave window. 

Atmospheric attenuation prevents an object’s total radiation from reaching the 

camera. If no correction for attenuation is applied, the measured apparent 

temperature will be lower and lower with increased distance. IR camera 

software corrects for atmospheric attenuation. Typically, LW cameras in the 

7.5μm to13.5μm range work well anywhere that atmospheric attenuation is 

involved, because the atmosphere tends to act as a high-pass filter above 

7.5μm (Figure 4). The MW band of 3–5μm tends to be employed with highly 

sensitive detectors for high end R&D and military applications. When 

acquiring a signal through the atmosphere with MW cameras, selected 

transmission bands must be used where less attenuation takes place. 

From other reading: Short-wave systems in particular are susceptible to 

attenuation by the atmosphere and, as a result, correction for relative 

humidity and distance to object are recommended. 



Figure 1.4. Atmospheric attenuation (white areas) with a chart of the gases 

and water vapor causing most of it. The areas under the curve represent the 

highest IR transmission. 



Foggy London Bridge 



Foggy London Bridge 





Figure 4. Atmospheric attenuation (white areas) with a chart of the gases and 

water vapor causing most of it. The areas under the curve represent the 



http://www.universe-galaxies-stars.com/infrared.html

Figure 4. Atmospheric attenuation (white areas) with a chart of the gases and 

water vapor causing most of it. The areas under the curve represent the 





Raleigh Scattering 

Rayleigh scattering, named after the British physicist Lord Rayleigh is the (dominantly) elastic scattering of light 

or other electromagnetic radiation by particles much smaller than the wavelength of the radiation. The Rayleigh 

scattering does not change the state of material hence it is a parametric process. The particles may be 

individual atoms or molecules. It can occur when light travels through transparent solids and liquids, but is most 

prominently seen in gases. Rayleigh scattering results from the electric polarizability of the particles. The 

oscillating electric field of a light wave acts on the charges within a particle, causing them to move at the same 

frequency. The particle therefore becomes a small radiating dipole whose radiation we see as scattered light. 

Rayleigh scattering of sunlight in the atmosphere causes diffuse sky radiation, which is the reason for the blue 

color of the sky and the yellow tone of the sun itself. 

For historical reasons, Rayleigh scattering of molecular nitrogen and oxygen in the atmosphere includes elastic 

scattering as well as the inelastic contribution from rotational Raman scattering in air, since the changes in 

wave number of the scattered photon are typically smaller than 50 cm−1. This can lead to changes in the 

rotational state of the molecules. Furthermore the inelastic contribution has the same wavelengths dependency 

as the elastic part. 

Scattering by particles similar to, or larger than, the wavelength of light is typically treated by the Mie theory, the 

discrete dipole approximation and other computational techniques. Rayleigh scattering applies to particles that 

are small with respect to wavelengths of light, and that are optically "soft" (i.e. with a refractive index close to 1). 

On the other hand, Anomalous Diffraction Theory applies to optically soft but larger particles. 


http://en.wikipedia.org/wiki/Rayleigh_scattering

Raleigh Scattering 

Elastic scattering of light or other electromagnetic radiation by particles much smaller than the wavelength of 

the radiation. 


http://en.wikipedia.org/wiki/Rayleigh_scattering

Temperature Measurements 

The radiation that impinges on the IR camera lens comes from three different 

sources. The camera receives radiation from the target object, plus radiation 

from its surroundings that has been reflected onto the object’s surface. Both 

of these radiation components become attenuated when they pass through 

the atmosphere. Since the atmosphere absorbs part of the radiation, it will 

also radiate some itself (Kirchhoff’s law). Given this situation, we can derive a 

formula for the calculation of the object’s temperature from a calibrated 

camera’s output. 



1. Emission from the object = Ɛ ∙τ∙W obj , 

Where Ɛ is the emissivity of the object (unit?) τ is the transmittance of the 

atmosphere. 

2. Reflected emission from ambient sources = (1 – Ɛ) ∙ τ∙W amb , 

Where (1 – Ɛ) is the reflectance of the object. (It is assumed that the ambient 

temperature T amb is the same for all emitting surfaces within the half sphere 

seen from a point on the object’s surface.) 

3. Emission from the atmosphere = (1 – τ) ∙ W atm , where (1 – τ) is the 

emissivity of the atmosphere. 

4. The total radiation power received by the camera can now be written: 

W tot = Ɛ ∙τ∙W obj + (1 – Ɛ) ∙ t ∙ W amb + (1 – τ) ∙ W atm , 

Where e is the object emissivity, τ is the transmission through the 

atmosphere, T amb is the (effective) temperature of the object’s surroundings, 

or the reflected ambient (background) temperature, and T atm is the 

temperature of the atmosphere. 



1. Emission from the object = (Ɛ obj 

) ∙ (τ atm 

∙ W obj 

), 

Where Ɛ obj 

is the emissivity of the object (unit?) τ atm 

is the transmittance of the 

atmosphere, W obj 

emitted energy fro the object. 

2. Reflected emission from ambient sources = (1 – Ɛ obj 

)∙(τ atm 

∙ W amb 

), 

Where (1 – Ɛ) is the reflectance of the object. (It is assumed that the ambient 

temperature T amb 

is the same for all emitting surfaces within the half sphere seen from 

a point on the object’s surface.), Wamb emitted energy of ambient source. 

3. Emission from the atmosphere = (1 – τ) ∙ W atm 

, where (1 – τ) is the emissivity of the 

atmosphere, W atm 

emitted energy from the atmosphere. 

4. The total radiation power received by the camera can now be written: 

W tot 

= Ɛ ∙τ∙W obj 

+ (1 – Ɛ) ∙ τ ∙ W amb 

+ (1 – τ) ∙ W atm 

, 

Where e is the object emissivity, τ is the transmission through the atmosphere, T amb 

is 

the (effective) temperature of the object’s surroundings, or the reflected ambient 

(background) temperature, and T atm 

is the temperature of the atmosphere. 



• absorbtion alpha (α), 

• reflection Rho (ρ), 

• transmission Tau (τ ), 

• emissivity epsilon (Ɛ). 

W amb 

1 = ρ obj + Ɛ obj ,where τ obj =0 

W obj 

W atm 

1 = τ atm + Ɛ atm ,where ρ atm =0 

W tot = Ɛ obj ∙τ atm ∙ W obj + (1 – Ɛ obj ) ∙ τ atm ∙ W amb + (1- τ atm ) ∙ W atm , 

Emission from object 

Reflection from ambient 

Emission from atmosphere 



To arrive at the correct target object temperature, IR camera software 

requires inputs for the emissivity of the object, atmospheric attenuation and 

temperature, and temperature of the ambient surroundings. Depending on 

circumstances, these factors may be measured, assumed, or found from 

look-up tables. 

Keywords: 

atmospheric attenuation (transmittance, τ atm ?) 



Chapter 2: IR Detectors For Thermographic Imaging 

IR Cameras 

Thermographic imaging is accomplished with a camera that converts infrared 

radiation (IR) into a visual image that depicts temperature variations across 

an object or scene. The main IR camera components are a lens, a detector in 

the form of a focal plane array (FPA), possibly a cooler for the detector, and 

the electronics and software for processing and displaying images (Figure 

2.1). Most detectors have a response curve that is narrower than the full IR 

range (900–14,000 nanometers or 0.9μm –14μm). Therefore, a detector (or 

camera) must be selected that has the appropriate response for the IR range 

of a user’s application. In addition to wavelength response, other important 

detector characteristics include sensitivity, the ease of creating it as a focal 

plane array with micrometer size pixels, and the degree of cooling required, if 

any. 



In most applications, the IR camera must view a radiating object through the 

atmosphere. Therefore, an overriding concern is matching the detector’s 

response curve to what is called an atmospheric window. This is the range of 

IR wavelengths that readily pass through the atmosphere with little 

attenuation. Essentially, there are two of these windows, one in the 2μm – 

5.6μm range, the short/ medium wavelength (SW/MW) IR band, and one in 

the 8μm – 14μm range, the long wavelength (LW) IR band. There are many 

detector materials and cameras with response curves that meet these criteria. 

Keywords: 

Atmospheric window 



Figure 2.1. Simplified block diagram of an IR camera 



Quantum vs. Non-Quantum Detectors 

The majority of IR cameras have a microbolometer type detector, mainly 

because of cost considerations. Microbolometer FPAs can be created from 

metal or semiconductor materials, and operate according to non-quantum 

principles. This means that they respond to radiant energy in a way that 

causes a change of state in the bulk material (i.e., the bolometer effect). 

Generally, microbolometers do not require cooling, which allows compact 

camera designs that are relatively low in cost. Other characteristics of 

microbolometers are: 

• Relatively low sensitivity (detectivity) 

• Broad (flat) response curve 

• Slow response time (time constant ~12 ms) 

For more demanding applications, quantum detectors are used, which 

operate on the basis of an intrinsic photoelectric effect. These materials 

respond to IR by absorbing photons that elevate the material’s electrons to a 

higher energy state, causing a change in conductivity, voltage, or current. By 

cooling these detectors to cryogenic temperatures, they can be very sensitive 

to the IR that is focused on them. 



They also react very quickly to changes in IR levels (i.e., temperatures), 

having a constant response time on the order of 1μs. Therefore, a camera 

with this type of detector is very useful in recording transient thermal events. 

Still, quantum detectors have response curves with detectivity that varies 

strongly with wavelength (Figure 2.2). Table 2.1 lists some of the most 

commonly used detectors in today’s IR cameras. 



Figure 2.2. Detectivity (D*) curves for different detector materials 



Quantum Well Infrared Photodetector 

A Quantum Well Infrared Photodetector (QWIP) is an infrared photodetector, 

which uses electronic inter subband transitions in quantum wells to absorb 

photons. The basic elements of a QWIP are quantum wells, which are 

separated by barriers. The quantum wells are designed to have one confined 

state inside the well and a first excited state which aligns with the top of the 

barrier. The wells are n-doped such that the ground state is filled with 

electrons. The barriers are wide enough to prevent quantum tunneling 

between the quantum wells. Typical QWIPs consists of 20 to 50 quantum 

wells. When a bias voltage is applied to the QWIP, the entire conduction band 

is tilted. Without light the electrons in the quantum wells just sit in the ground 

state. When the QWIP is illuminated with light of the same or higher energy 

as the inter subband transition energy, an electron is excited. 


http://en.wikipedia.org/wiki/Quantum_well_infrared_photodetector

Quantum Well Infrared Photodetector 


http://en.wikipedia.org/wiki/Quantum_well_infrared_photodetector

Bolometer Effect 

A bolometer, meaning measurer of thrown things is a device for measuring the power of incident 

electromagnetic radiation via the heating of a material with a temperature-dependent electrical 

resistance. It was invented in 1878 by the American astronomer Samuel Pierpont Langley. The name 

comes from the Greek word bole, for something thrown, as with a ray of light 

Principle of operation 

A bolometer consists of an absorptive element, such as a thin layer of metal, connected to a thermal 

reservoir (a body of constant temperature) through a thermal link. The result is that any radiation 

impinging on the absorptive element raises its temperature above that of the reservoir — the greater 

the absorbed power, the higher the temperature. The intrinsic thermal time constant, which sets the 

speed of the detector, is equal to the ratio of the heat capacity of the absorptive element to the thermal 

conductance between the absorptive element and the reservoir.[2] The temperature change can be 

measured directly with an attached resistive thermometer, or the resistance of the absorptive element 

itself can be used as a thermometer. Metal bolometers usually work without cooling. They are produced 

from thin foils or metal films. Today, most bolometers use semiconductor or superconductor absorptive 

elements rather than metals. These devices can be operated at cryogenic temperatures, enabling 

significantly greater sensitivity. 

Bolometers are directly sensitive to the energy left inside the absorber. For this reason they can be 

used not only for ionizing particles and photons, but also for non-ionizing particles, any sort of radiation, 

and even to search for unknown forms of mass or energy (like dark matter); this lack of discrimination 

can also be a shortcoming. The most sensitive bolometers are very slow to reset (i.e., return to thermal 

equilibrium with the environment). On the other hand, compared to more conventional particle detectors, 

they are extremely efficient in energy resolution and in sensitivity. They are also known as thermal 

detectors. 


Conceptual schematic of a bolometer. 

Power P from an incident signal is 

absorbed by the bolometer and heats 

up a thermal mass with heat capacity 

C and temperature T. The thermal 

mass is connected to a reservoir of 

constant temperature through a link 

with thermal conductance G. The 

temperature increase is ΔT = P/G. 

The change in temperature is read 

out with a resistive thermometer. The 

intrinsic thermal time constant is τ = 

C/G. 


In short: Bolometer Effect 

A bolometer, is a device for measuring the power of incident electromagnetic 

radiation via the heating of a material with a temperature-dependent electrical 

resistance. 


Bolometer: 

The design of cosmic microwave background experiments is a very challenging task. The greatest problems are the receivers, the 

telescope optics and the atmosphere. Many improved microwave amplifier technologies have been designed for microwave 

background applications. Some technologies used are HEMT, MMIC, SIS and bolometers. Experiments generally use elaborate 

cryogenic systems to keep the amplifiers cool. Often, experiments are interferometers which only measure the spatial fluctuations 

in signals on the sky, and are insensitive to the average 2.7 K background. 


Table 2.1. Detector types and materials commonly used in IR cameras. 

The Kelvin is a unit of measure for temperature based upon an absolute scale. It is one of the seven base units in the International System of Units (SI) and 

is assigned the unit symbol K. The Kelvin scale is an absolute, thermodynamic temperature scale using as its null point absolute zero, the temperature at 

which all thermal motion ceases in the classical description of thermodynamics. The Kelvin is defined as the fraction 1⁄273.16 of the thermodynamic 

temperature of the triple point of water (exactly 0.01 °C or 32.018 °F). In other words, it is defined such that the triple point of water or approximately 0ºC is 

exactly 273.16 K 



LW Photon Detector 77K. 

■ 

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Broad Band IR- The extended spectral area of an MWIR camera simplifies 

the spatial assignment of gas plumes to visible objects. 


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MW Photon Detector 77K. 

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InGaAs Detectors & Avalanche Photodiodes 

Large area & high sensitivity from 800 to 2200nm 

Pro-Lite offers Indium Gallium Arsenide (InGaAs) detectors and avalanche photodiodes (APD) from GPD Optoelectronics 

Corporation. InGaAs detectors operate from 800 to 1700nm and are similar to Ge but with enhanced sensitivites at low light levels. 

Available options include large area InGaAs (up to 5mm), custom packages and submounts, custom filters and windows, 

extended range InGaAs (to 2.2µm) and high speed InGaAs (up to 4GHz). GPD also produces InGaAs avalanche photodiodes 

(APD) with active areas of 80 and 200µm. An APD can be regarded as the semiconductor equivalent of the photomultiplier tube 

(PMT) detector. With the application of a reverse bias Voltage, an APD experiences a high internal current gain, making an 

avalanche photodiode significantly more sensitive to low light levels than a regular InGaAs detector. Our APDs can be supplied as 

chip/submount combinations, in TO-46 cases with flat windows or ball lenses and as fibre pigtailed packages. 


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Germanium Detectors & Avalanche Photodiodes 

Economical near-infrared detection from 800 to 1800nm 

Pro-Lite offers Germanium (Ge) detectors and avalanche photodiodes (APDs) from GPD Optoelectronics Corporation. Ge 

detectors operate from 800 to 1800nm and are similar to InGaAs but have a reduced sensitivity, are more economical and provide 

improved linearity at high levels of irradiance. Available options include active areas from 0.1 to 13mm diameter, two-colour 

silicon/germanium (Si/Ge) detectors, cryogenic cooling (TE coolers & dewars), custom packages and submounts, custom filters 

and windows and avalanche Ge photodiodes. 


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Operating Principles for Quantum Detectors 

In materials used for quantum detectors, at room temperature there are 

electrons at different energy levels. Some electrons have sufficient thermal 

energy that they are in the conduction band, meaning the electrons there are 

free to move and the material can conduct an electrical current. Most of the 

electrons, however, are found in the valence band, where they do not carry 

any current because they cannot move freely. (See left-most views of Fig 2.3.) 

When the material is cooled to a low enough temperature, which varies with 

the chosen material, the thermal energy of the electrons may be so low that 

there are none in the conduction band (upper center view of Figure 2.3). 

Hence the material cannot carry any current. When these materials are 

exposed to incident photons, and the photons have sufficient energy, this 

energy can stimulate an electron in the valence band, causing it to move up 

into the conduction band (upper right view of Figure 2.3). 



Figure 2.3. Operating principle of quantum detectors 



QWIP FPA mounted on a ceramic substrate and bonded to external 

electronics. 



Thus the material (the detector) can carry a photocurrent, which is 

proportional to the intensity of the incident radiation. There is a very exact 

lowest energy of the incident photons that will allow an electron to jump from 

the valence band into the conduction band. This energy is related to a certain 

wavelength, the cutoff wavelength. Since photon energy is inversely 

proportional to its wavelength, the energies are higher in the SW/MW band 

than in the LW band. Therefore, as a rule, the operating temperatures for LW 

detectors are lower than for SW/MW detectors. For an InSb MW detector, the 

necessary temperature must be less than 173 K (–100°C), although it may be 

operated at a much lower temperature. An HgCdTe (MCT) LW detector must 

be cooled to 77 K (–196°C) or lower. A QWIP detector typically needs to 

operate at about 70 K (–203°C) or lower. The lower center and right views of 

Figure 3 depict quantum detector wavelength dependence. The incident 

photon wavelength and energy must be sufficient to overcome the band gap 

energy, ΔE. 



Cooling Methods 

The first detectors used in infrared radiometric instruments were cooled with 

liquid nitrogen. The detector was attached to the Dewar flask that held the 

liquid nitrogen, thus keeping the detector at a very stable and low temperature 

(-196°C). Later, other cooling methods were developed. The first solid-state 

solution to the cooling problem was presented by AGEMA in 1986, when it 

introduced a Peltier effect cooler for a commercial IR camera. In a Peltier 

cooler, DC current is forced through a thermoelectric material, removing heat 

from one junction and creating a cold side and a hot side. The hot side is 

connected to a heat sink, whereas the cold side cools the component 

attached to it. 


http://www.techbriefs.com/component/content/article/124-ntb/media/supplements/3117

Cooling Methods 

The first detectors used in infrared radiometric instruments were cooled with liquid 

nitrogen. The detector was attached to the Dewar flask that held the liquid nitrogen, 

thus keeping the detector at a very stable and low temperature (-196°C). Later, other 

cooling methods were developed. The first solid-state solution to the cooling problem 

was presented by AGEMA in 1986, when it introduced a Peltier effect cooler for a 

commercial IR camera. In a Peltier cooler, DC current is forced through a 

thermoelectric material, removing heat from one junction and creating a cold side and 

a hot side. The hot side is connected to a heat sink, whereas the cold side cools the 

component attached to it. See Figures 2.4 and 2.5. 

For very demanding applications, where the highest possible sensitivity was needed, 

an electrical solution to cryogenic cooling was developed. This resulted in the Stirling 

cooler. Only in the last 15 to 20 years were manufacturers able to extend the life of 

Stirling coolers to 8,000 hours or more, which is sufficient for use in thermal cameras. 

The Stirling process removes heat from the cold finger (Figure 2.6) and dissipates it at 

the warm side. The efficiency of this type of cooler is relatively low, but good enough 

for cooling an IR camera detector. Regardless of the cooling method, the detector 

focal plane is attached to the cold side of the cooler in a way that allows efficient 

conductive heat exchange. Because focal plane arrays are small, the attachment area 

and the cooler itself can be relatively small. 



Figure 2.4. Single stage Peltier cooler 



Figure 2.5. Three-stage Peltier cooler 



Figure 2.6. Integrated Stirling cooler, working with to external electronics 

helium gas, cooling down to -196 ºC or sometimes even lower temperatures 



Focal Plane Array Assemblies 

Depending on the size/resolution of an FPA assembly, it has from (approximately) 

60,000 to more than 1,000,000 individual detectors. For the sake of simplicity, this can 

be described as a two-dimensional pixel matrix with each pixel (detector) having 

micrometer size dimensions. FPA resolutions can range from about 160 × 120 pixels 

up to 1024 × 1024 pixels. In reality, assemblies are a bit more complex. Depending 

on the detector material and its operating principle, an optical grating may be part of 

the FPA assembly. This is the case for QWIP detectors, in which the optical grating 

disperses incident radiation to take advantage of directional sensitivity in the detector 

material’s crystal lattice. This has the effect of increasing overall sensitivity of a QWIP 

detector. Furthermore, the FPA must be bonded to the IR camera readout electronics. 

A finished QWIP detector and IC electronics assembly is shown in Figure 8. This 

would be incorporated with a Dewar or Stirling cooler in an assembly similar to those 

shown in Figure 7. Another complexity is the fact that each individual detector in the 

FPA has a slightly different gain and zero offset. To create a useful thermographic 

image, the different gains and offsets must be corrected to a normalized value. This 

multi-step calibration process is performed by the camera software. See Figures 2.9 – 

2.11. The ultimate result is a thermographic image that accurately portrays relative 

temperatures across the target object or scene (Figure 2.12). Moreover, actual 

temperatures can be calculated to within approximately ±1°C accuracy. 



Figure 2.7. Examples of cooled focal plane array assemblies used in IR 

cameras 



Figure 2.8. QWIP FPA mounted on a ceramics substrate and bonded to 

external electronics 



Figure 2.9. To normalize different FPA detector gains and offsets, the first 

correction step is offset compensation. This brings each detector response 

within the dynamic range of the camera’s A/D converter electronics. 



Figure 2.10. After offset compensation, slope correction is applied. 



Figure 2.11. After gain factors are brought to the same value, non-uniformity 

correction (NUC) is applied so that all detectors have essentially the same 

electronic characteristics. 



Figure 2.12. IR image from a 1024 × 1024 InSb detector camera (MW PD) 



High-end InSb cameras 

ImageIR ® 8300/9300 Z 

for long-range 

identification 


http://www.opli.net/opli_magazine/eo/2013/first-thermal-camera-features-superzoom-lens-and-hd-resolution-apr/

Application Criteria 

As indicated earlier, different types of detectors have different thermal and 

spectral sensitivities. In addition, they have different cost structures due to 

various degrees of manufacturability. Where they otherwise fit the application, 

photon detectors such as InSb and QWIP types offer a number of advantages: 

• High thermal sensitivity 

• High uniformity of the detectors, i.e., very low fixed pattern noise 

• There is a degree of selectability in their spectral sensitivity 

• High yield in the production process 

• Relatively low cost 

• They are resistant to high temperatures and high radiation 

• They produce very good image quality 



Camera electronics can handle wide variations in absolute detector 

sensitivities. For example, high sensitivity that might saturate a detector at 

high thermal intensities can be handled by aperture control and neutral 

density filters. Both of these solutions can reduce the radiant energy 

impinging on the FPA. 

Price aside, spectral sensitivity is often an overriding concern in selecting a 

detector and camera for a specific application. Once a detector is selected, 

lens material and filters can be selected to somewhat alter the overall 

response characteristics of an IR camera system. Fig 2.13 shows the system 

response for a number of different detectors. 



Figure 2.13. Relative response 

curves for a number of IR cameras 



Chapter 3: Getting The Most From Your IR Camera 

Understanding IR camera calibration and corrections help ensure accurate 

temperature measurements and thermographic mapping. 

Quantitative Measurements with IR Cameras 

For best results, IR camera users need to think carefully about the type of 

measurements they need to make, and then be proactive in the camera’s 

calibration process. Of course, the first step is selecting a camera with the 

appropriate features and software for the application. An understanding of the 

differences between (1) thermographic and (2) radiometric measurements is 

very helpful in this regard. 

Thermography is a type of infrared imaging in which IR cameras detect 

radiation in the electromagnetic spectrum with wavelengths from roughly 900 

to 14,000 nanometers (0.9 μm –14 μm ) and produce images of that radiation. 

Typically, this imaging is used to measure temperature variations across an 

object or scene, which can be expressed in degrees Celsius, Fahrenheit, or 

Kelvin. 



Radiometry is the measurement of radiant electromagnetic energy, 

especially that associated with the IR spectrum. It can be more simply defined 

as an absolute measurement of radiant flux. The typical unit of measure for 

imaging radiometry is radiance, which is expressed in units of Watts/(sr-cm2). 

(The abbreviation “sr” stands for steradian; a non-dimensional geometric ratio 

expressing the solid (conical) angle that encloses a portion of the surface of a 

sphere equivalent to the square of the radius.) 

In simple terms, one can think of thermography as “how hot” an object 

is, whereas radiometry is “how much energy” the object is giving off. 

Although these two concepts are related, they are not the same thing. IR 

cameras inherently measure irradiance not temperature, but thermography 

does stem from radiance. When you thermographically calibrate an IR system 

you are calibrating /measuring based on effective blackbody radiance and 

temperature. Therefore, the emissivity of the target object you are measuring 

is vital to achieving accurate temperatures. (Emissivity or emittance is the 

radiative property of an object relative to a perfect blackbody.) 



Entry level IR cameras with microbolometer detectors operate according to 

non-quantum principles. The detectors respond to radiant energy in a way 

that causes a change of state in the bulk material (e.g., resistance or 

capacitance). Calibration software in these cameras is oriented toward 

thermographic imaging and temperature measurements. High end IR 

cameras with photon detectors operate according to quantum physics 

principles. Although they also provide high quality images, their software is 

typically more sophisticated, allowing accurate measurements of both 

radiance and temperature. 



Some reasons why radiance measurements are important include: 

1. Given a linear sensor, measured radiance is linear with incident energy. 

Temperature is non-linear with raw digital image counts, even with a linear 

sensor. 

2. Given the radiance and area of an object, radiant intensity can be 

calculated. Knowing total radiant intensity of a target gives a radiometric 

analyst the ability to model the irradiance generated by the target over 

various geometric and atmospheric conditions. 

3. The relationship between spectral bands of interest can be much easier to 

determine if you are working within radiometric units. 

4. The comparison between different objects in radiometric terms tends to 

have less uncertainty because emissivity is not a concern. (One still needs 

to consider atmospheric and spectral band-pass effects.) 

5. One can typically convert a radiometric signature from radiance to 

effective blackbody temperature given a few assumptions or ancillary 

measurement data. It tends to be more difficult to go from temperature to 

radiance. 



Key Physical Relationships in Camera Operation 

There are five basic steps in producing radiometric and thermographic 

measurements with an IR camera system: 

1. The target object has a certain energy signature that is collected by the IR 

camera through its lens. 

2. This involves the collection of photons in the case of a photon detector, or 

collection of heat energy with a thermal detector, such as a 

microbolometer. 

3. The collected energy causes the detector to produce a signal voltage that 

results in a digital count through the system’s A/D converter. (For example, 

a FLIR ThermoVisionR SC6000 IR camera has a 14-bit dynamic range in 

its A/D converter, which creates count values ranging from 0–16,383. The 

more IR energy incident on the camera’s detector (within its spectral band), 

the higher the digital count.) 

4. When the camera is properly calibrated, digital counts are transformed into 

radiance values. 

5. Finally, the calibrated camera‘s electronics convert radiance values to 

temperature using the known or measured emissivity of the target object. 



Expanding on Steps 4 and 5, an effective blackbody temperature 

measurement can be derived from a radiance measurement by applying a 

radiometric calibration, temperature vs. radiance model, and emissivity of the 

target object or scene. Every IR camera designed for serious measurements 

is calibrated at the factory. In the calibration lab, the camera takes a number 

of blackbody measurements at known temperatures, radiance levels, 

emissivities, and distances. This creates a table of values based on the A/D 

counts from the temperature/radiance measurements. 

Once the counts for each blackbody temperature measurement are entered 

into the calibration software, the data are then passed through an in-band 

radiance curve fit algorithm to produce the appropriate in-band radiance vs. 

count values given the camera system’s normalized spectral response 

function. This produces a radiometric calibration of in-band radiance [W/(srcm2)] 

versus the digital counts obtained while viewing a blackbody over a 

range of temperatures. The result is a series of calibration curves. An 

example of how calibration points are captured is shown in Figure 3.1. 



Figure 3.1. Example of camera measurements and corresponding in-band 

radiance values for given black body temperatures with resulting radiance vs. 

measurement curve. 



The calibration curves are stored in the camera system’s memory as a series 

of numeric curve-fit tables that relate radiance values to blackbody 

temperatures. When the system makes a measurement, it takes the digital 

value of the signal at a given moment, goes into the appropriate calibration 

table, and calculates temperature. Due consideration is given to other factors 

like atmospheric attenuation, reflected ambient temperature, and the 

camera’s ambient temperature drift before the final result is presented. 



Ambient Drift Compensation (ADC). 

Another important consideration in the calibration process is the radiation 

caused by the heating and cooling of the camera itself. Any swings in camera 

internal temperature caused by changes in environment or the heating and 

cooling of camera electronics will affect the radiation intensity at the detector. 

The radiation that results directly from the camera is called parasitic radiation 

and can cause inaccuracies in camera measurement output, especially with 

thermographically calibrated cameras. Certain IR cameras (like the FLIR 

ThermoVisionR product line), have internal sensors that monitor changes in 

camera temperature. As part of the calibration process, these cameras are 

placed in an environmental chamber and focused at a black body reference. 

The temperature of the chamber and black body are then varied and data is 

collected from the internal sensors. Correction factors are then created and 

stored in the camera. In real-time operation, the camera sensors continually 

monitor internal temperature and send feedback to the camera processor. 

The camera output is then corrected for any parasitic radiation influences. 

This functionality is commonly referred to as ambient drift compensation. 



Ultimately, the camera must calculate at an object’s temperature based on: 

(1) its emission, 

(2) reflected emission from ambient sources, and 

(3) emission from the atmosphere 

using the Total Radiation Law. The total radiation power received by the 

camera can be expressed as: 

W tot = Ɛ ∙τ∙W obj + (1 – Ɛ) ∙ τ∙W amb + (1 – τ) ∙ W atm , 

Where: 

Ɛ is the object emissivity, τ is the transmission through the atmosphere, T amb 

is the (effective) temperature of the object surroundings, or the reflected 

ambient (background) temperature, and T atm is the temperature of the 

atmosphere. 



The best results are obtained when a user is diligent in entering known values for all 

the pertinent variables into the camera software. Emissivity tables are available for a 

wide variety of common substances. However, when in doubt, measurements should 

be made to obtain the correct values. 

Calibration and analysis software tools available to users are not always contained 

onboard the camera. While high-end cameras have many built-in software functions, 

others rely on external software that runs on a PC. Even high-end cameras are 

connected to PCs to expand their internal calibration, correction, and analysis 

capabilities. For example, FLIR’s ThermaCAMR RTools software can serve a wide 

variety of functions from real-time image acquisition to post-acquisition analysis. 

Whether the software is on the camera or an external PC, the most useful packages 

allow a user to easily modify calibration variables. For instance, FLIR’s ThermaCAM 

RTools provides the ability to enter and modify emissivity, atmospheric conditions, 

distances, and other ancillary data needed to calculate and represent the exact 

temperature of the object, both live and through saved data. This software provides a 

post-measurement capability to further modify atmospheric conditions, spectral 

responsivity, atmospheric transmission changes, internal and external filters, and 

other important criteria as needed. The discussions that follow below are intended to 

represent both onboard and external camera firmware and software functions. Where 

these functions reside depends on the camera. 



Typical Camera Measurement Functions IR cameras have various operating 

modes to assure correct temperature measurements under different 

application conditions. Typical measurement functions include: 

• Spotmeter 

•Area 

• Profile 

• Isotherm 

• Temperature range 

• Color or gray scale settings 

Cursor functions allow easy selection of an area of interest, such as the 

crosshairs of the spot readings in Figure 3.2. In addition, the cursor may be 

able to select circle, square, and irregularly shaped polygon areas, or create a 

line for a temperature profile. Once an area is selected, it can be “frozen” so 

that the camera can take a snapshot of that area. Alternatively, the camera 

image can remain live for observation of changes in temperature. 



Figure 3.2. IR image of a printed circuit board indicating three spot 

temperature readings. Image colors correspond to the temperature scale on 

the right. 



The spotmeter finds the temperature at a particular point. Depending on the 

camera, this function may allow ten or more movable spots, one or more of 

which may automatically find the hottest point in the image. The area function 

isolates a selected area of an object or scene and finds the maximum, 

minimum, and average temperatures inside that area. The isotherm function 

makes it possible to portray the temperature distribution of a hot area. 

Multiple isotherms may be allowed. The line profile is a way to visualize the 

temperature along some part of the object, which may also be shown as a 

graph (Figure 3.3). 

Figure 3.3. Graph of temperature along a selected area of a target object 

using a camera’s profile function 



The temperature measurement range typically is selectable by the user. This 

is a valuable feature when a scene has a temperature range narrower than a 

camera’s full-scale range. Setting a narrower range allows better resolution of 

the images and higher accuracy in the measured temperatures. Therefore, 

images will better illustrate smaller temperature differences. On the other 

hand, a broader scale and/or higher maximum temperature range may be 

needed to prevent saturation of the portion of the image at the highest 

temperature. As an adjunct to the temperature range selection, most cameras 

allow a user to set up a color scale or gray scale to optimize the camera 

image. 

Figure 3.4 illustrates two gray scale possibilities. In Figure 3.2 a so-called 

“iron scale” was used for a color rendering. In a manner similar to the gray 

scale used in Figure 4, the hottest temperatures can be rendered as either 

lighter colors or darker colors. Another possibility is rendering images with 

what is known as a rainbow scale (Figure 3.5). In some color images, gray is 

used to indicate areas where the camera detector has become saturated (i.e., 

temperatures well above the top of the scale). 



Figure 3.4. Gray scale images of car engine; left view has white as the 

hottest temperature; right view shows black as the hottest 



Figure 3.5. Rainbow scale showing lower temperatures towards the blue end 

of the spectrum 



While choice of color scale is often a matter of personal preference, there 

may be times when one type of scale is better than another for illustrating the 

range of temperatures in a scene. In the case of isotherm measurements, 

areas with the same thermal radiance are highlighted. If we use a color scale 

with ten colors, we will in fact get ten isotherms in the image. Such a scale 

sometimes makes it easier to see the temperature distribution over an object. 

In Figure 3.6, the temperature scale is selected so that each color is an 

isotherm with a width of 2°C. Still, it is important to realize that an isothermal 

temperature scale rendering will not be accurate (1) unless all of the 

highlighted area has the same emissivity, and (2) the ambient temperatures 

are the same for all objects within the area. This points out common problems 

for IR camera users. Often, emissivity varies across an object or scene, along 

with variations in ambient temperatures, accompanied by atmospheric 

conditions that don’t match a camera’s default values. This is why IR cameras 

include measurement correction and calibration functions. 



Figure 3.6. Isotherm color scale with each color having an isotherm width of 

2°C 



Emissivity Corrections 

In most applications, the emissivity of an object is based on values found in a 

table. Although camera software may include an emissivity table, users 

usually have the capability of inputting emissivity values for an object ranging 

from 0.1 to 1.0. Many cameras also provide automatic corrections based on 

user input for reflected ambient temperature, viewing distance, relative 

humidity, atmospheric transmission, and external optics. As described earlier, 

the IR camera calculates a temperature based on radiance measurements 

and the object’s emissivity. However, when the emissivity value is unknown 

or uncertain, the reverse process can be applied. Knowing the object 

temperature, emissivity can be calculated. This is usually done when exact 

emissivity values are needed. There are two common methods of doing this. 

The first method establishes a known temperature by using an equalization 

box. This is essentially a tightly controlled temperature chamber with 

circulating hot air. The length of time in the box must be sufficient to allow the 

whole object to be at a uniform temperature. In addition, it is absolutely 

necessary that the object stabilize at a temperature different from the 

surroundings where the actual measurements will take place. Usually, the 

object is heated to a temperature at least 10°C above the surroundings to 

ensure that the thermodynamics of the measurements are valid. 



Once the object has reached the set temperature, the lid is drawn off and a 

thermogram is captured of the object. The camera and/or software for 

processing thermograms can be used to get the emissivity value. 

Another (“adjacent spot”) method is much simpler, but still gives reasonably 

exact values of the emissivity. It uses an area of known emissivity. The idea is 

to determine the temperature of the object with the camera in the usual way. 

The object is adjusted so that the area with unknown emissivity is very close 

to an area of known emissivity. The distance separating these areas must be 

so small that it can be safely assumed they have the same temperature. From 

this temperature measurement the unknown emissivity can be calculated. 

The problem is illustrated in Figure 7, which is an image of a printed circuit 

board (PCB) heated to a uniform temperature of 68.7°C. However, areas of 

different emissivities may actually have different temperatures, as indicated in 

the caption of Figure 3.7a. Using the technique just described, emissivity 

correction proceeds by finding a reference spot where a temperature of 

68.7°C is indicated and calculating the emissivity at that location. By knowing 

the emissivity of the reference spot, the emissivity of the target spots can be 

calculated. The corrected temperatures are shown in Figure 3.7b. 



Figure 3.7a. PCB heated to a uniform 68.7°C, but digital readouts are 

incorrect. 



Figure 3.7b. PCB with emissivity correction using the “adjacent spot” 

technique. Digital readouts now indicate the correct temperatures at all 

locations. 



As illustrated in these figures, this technique can be used with a camera’s 

area selection function (“AR” in the figures) and using the average 

temperature for that area. The reason for using the average temperature in 

the reference area is that there is usually a spread of temperatures within the 

area, especially for materials with low emissivity. In that case, using a 

spotmeter or an area maximum value would give a less stable result. The 

isotherm function is not recommended either, as it is not possible to get the 

averaging effect with it. It may also be possible to use a contact sensor to find 

the temperature of an area of unknown emissivity, but such measurements 

pose other problems that may not be easy to overcome. 

Furthermore, it is never possible to measure the emissivity of an object whose 

temperature is the same as the reflected ambient temperature from its 

surroundings. Generally, a user can also input other variables that are 

needed to correct for ambient conditions. These include factors for ambient 

temperatures and atmospheric attenuation around the target object. 



Using Camera Specifications When considering IR camera performance, 

most users are interested in how small an object or area can be detected and 

accurately measured at a given distance. Knowing a camera’s field of view 

(FOV) specifications helps determine this. 

Field of View (FOV). This parameter depends on the camera lens and focal 

plane dimensions, and is expressed in degrees, such as 35.5° ×28.7° or 

18.2 × 14.6°. For a given viewing distance, this determines the dimensions of 

the total surface area “seen” by the instrument (Figure 3.8). For example, a 

FLIR ThermoVision SC6000 camera with a 25mm lens has an FOV of 0.64 × 

0.51 meters at a distance of one meter, and 6.4 × 5.1 meters at a distance of 

ten meters. 



Figure 3.8. A camera’s field of view (FOV) varies with viewing distance. 



Instantaneous Field of View (IFOV). 

This is a measure of the spatial resolution of a camera’s focal plane array 

(FPA) detector. The configuration of the FPA in the FLIR ThermoVision 

SC6000 is 640 × 512 detectors, which makes a total of 327,680 individual 

picture elements (pixels). Suppose you are looking at an object at a distance 

of one meter with this camera. In determining the smallest detectable object, 

it is important to know the area’s IFOV covered by an individual pixel in the 

array. The total FOV is 0.64 × 0.51 meters at a distance of one meter. If we 

divide these FOV dimensions by the number of pixels in a line and row, 

respectively, we find that a pixel’s IFOV is an area approximately 1.0 × 

1.0mm at that distance. Figure 3.9 illustrates this concept. 



Figure 3.9. A camera’s geometric (spatial) resolution (IFOV) is determined by 

its lens and FPA configuration. 



To use this information consider, the pixel IFOV relative to the target object 

size (Figure 3.10). In the left view of this figure, the area of the object to be 

measured covers the IFOV completely. Therefore, the pixel will receive 

radiation only from the object, and its temperature can be measured correctly. 

In the right view of Figure 3.10, the pixel covers more than the target object 

area and will pick up radiation from extraneous objects. If the object is hotter 

than the objects beside or behind it, the temperature reading will be too low, 

and vice versa. Therefore it is important to estimate the size of the target 

object compared to the IFOV in each measurement situation. 

Figure 3.10. IFOV (red squares) relative to object size. 



Spot Size Ratio (SSR). 

At the start of a measurement session, the distance between the camera and 

the target object should be considered explicitly. For cameras that do not 

have a calibrated spot size, the spot size ratio method can be used to 

optimize measurement results. SSR is a number that tells how far the camera 

can be from a target object of a given size in order to get a good temperature 

measurement. A typical figure might be 1,000:1 (also written 1,000/1, or 

simply abbreviated as 1,000). This can be interpreted as follows: at 1000 mm 

distance from a target, the camera will measure a temperature averaged over 

a 1mm square. Note that SSR is not just for targets far away. It can be just as 

important for close-up work. However, the camera’s minimum focal distance 

must also be considered. For shorter target distances, some manufacturers 

offer close-up lenses. 



For any application and camera/lens combination, the following equation 

applies: 

D/S = SSR/1 (or more conveniently S = D/SSR) 

Where: 

D is the distance from the camera to the target, 

S is smallest target dimension of interest, and 

SSR is the spot size ratio. 

The units of D and S must be the same. When selecting a camera, keep in 

mind that IFOV is a good figure of merit to use. The smaller the IFOV, the 

better the camera for a given total field of view. 



Other Tools for Camera Users 

As mentioned earlier, IR cameras are calibrated at the factory, and field 

calibration in not practical. However, some cameras have a built-in blackbody 

to allow a quick calibration check. These checks should be done periodically 

to assure valid measurements. Bundled and optional data acquisition 

software available for IR cameras allows easy data capture, viewing, analysis, 

and storage. Software functions may include real-time radiometric output of 

radiance, radiant intensity, temperature, target length/area, etc. Optional 

software modules are also available for spatial and spectral radiometric 

calibration 空间与光谱辐射测量标定 . 

Functions provided by these modules might include: 

• Instrument calibration in terms of radiance, irradiance, and temperature 

• Radiometric data needed to set instrument sensitivity and spectral range 

• Use of different transmission and/or emissivity curves or constants for 

calibration data points 

• Adjustments for atmospheric effects 



In addition, IR camera software and firmware provide other user inputs that 

refine the accuracy of temperature measurements. One of the most important 

functions is non-uniformity correction (NUC) of the detector FPA. This type of 

correction is needed due to the fact that each individual detector in the 

camera’s FPA has a slightly different gain and zero offset. To create a useful 

thermographic image, the different gains and offsets must be corrected to a 

normalized value. This multi-step NUC process is performed by camera 

software. However, some software allows the user to specify the manner in 

which NUC is performed by selecting from a list of menu options. For 

example, a user may be able to specify either a one-point or a twopoint 

correction. 

■ 

■ 

A one-point correction only deals with pixel offset. 

Two-point corrections perform both gain and offset normalization of 

pixel- to-pixel nonuniformity. 



With regard to NUC, another important consideration is how this function 

deals with the imperfections that most FPAs have as a result of 

semiconductor wafer processing. Some of these imperfections are manifested 

as bad pixels that produce no output signals or as outputs far outside of a 

correctable range. Ideally, the NUC process identifies bad pixels and replaces 

them using a nearest neighbor replacement algorithm. Bad pixels are 

identified based on a response and/or offset level outside user-defined points 

from the mean response and absolute offset level. Other NUC functions may 

be included with this type of software, which are too numerous to mention. 

The same is true of many other off-the-shelf software modules that can be 

purchased to facilitate thermographic image display, analysis, data file 

storage, manipulation, and editing. Availability of compatible software is an 

important consideration when selecting an IR camera for a user’s application 

or work environment. 



Conclusions 

Recent advances in IR cameras have made them much easier to use. 

Camera firmware has made setup and operation as simple as using a 

conventional video camera. Onboard and PC-based software provides 

powerful measurement and analysis tools. Nevertheless, for accurate results, 

the user should have an understanding of IR camera optical principals and 

calibration methods. At the very least, the emissivity of a target object should 

be entered into the camera’s database, if not already available as a table 

entry. 

Keywords: 

At the very least, the emissivity of a target object should be entered into the 

camera’s database, if not already available as a table entry. 



Chapter 4: Filters Extend IR Camera Usefulness Where 

Filters Can Help 

Materials that are transparent or opaque to IR wavelengths present problems 

in non-contact temperature measurements with an IR camera. With 

transparent materials, the camera sees through them and records a 

temperature that is a combination of the material itself and that which is 

behind it. In the second case, when an IR camera needs to see through a 

material to measure the temperature of an object behind it, signal attenuation 

and ambient reflections can make accurate temperature readings difficult or 

impossible. In some cases, an IR filter can be placed in the camera’s optical 

path to overcome these problems. 



IR Window: Cordex IR Window 


Spectral Response is the Key 

IR cameras inherently measure irradiance not temperature. However, a 

camera’s software coverts radiance measurements into temperatures by 

using the known emissivity of a target object and applying internal calibration 

data for the camera’s spectral response. The spectral response is determined 

primarily by the camera’s lens and detector. Figure 4.1 shows the spectral 

response of a few IR cameras with various spectral responses. The spectral 

performance of most cameras can be found in their user manual or technical 

specifications. For many objects, emissivity is a function of their radiance 

wavelength, and is further influenced by their temperature, the angle at which 

they are viewed by a camera, and other factors. An object whose emissivity 

varies strongly with wavelength is called a selective radiator. One that has the 

same emissivity for all wavelengths is called a greybody. Transparent 

materials, such as glass and many plastics, tend to be selective radiators. In 

other words, their degree of transparency varies with wavelength. There may 

be IR wavelengths where they are essentially opaque due to absorption. 

Since, according to Kirchhoff’s Law, a good absorber is also a good emitter, 

this opens the possibility of measuring the radiance and temperature of a 

selective radiator at some wavelength. 



Figure 4.1. Relative response curves for a number of IR cameras 



Spectral Adaptation 

Inserting a spectral filter into the camera’s optical path is called spectral 

adaptation. The first step of this process is to analyze the spectral properties 

of the semitransparent material you are trying to measure. For common 

materials the data may be available in published data. Otherwise, this 

requires analysis with a spectrophotometer. (The camera manufacturer or a 

consulting firm may supply this service.) In either case, the objective is to find 

the spectral location of a band of complete absorption that falls within the IR 

camera’s response curve. Microbolometer detectors have rather broad 

response curves so they are not likely to present a problem in this respect. 

However, adding a filter decreases overall sensitivity due to narrowing of the 

camera’s spectral range. Sensitivity is reduced approximately by the ratio of 

the area under the filter’s spectral curve to the area under the camera’s 

spectral curve. 



This could be a problem for microbolometer systems, since they have 

relatively low sensitivity to start with and a broad spectral curve. Using a 

camera with, for example, a QWIP detector will provide greater sensitivity with 

a narrower spectral curve. Still, this narrow range may limit the application of 

such cameras for spectral adaptation. Ultimately, an optical (IR) filter must be 

selected that blocks all wavelengths except the band where the object 

absorbs. This ensures that the object has high emissivity within that band. 

Besides semitransparent solids, selective adaptation can also be applied to 

gases. However, a very narrow filter might be required for selecting an 

absorption “spike” in a gas. Even with proper filtering, temperature 

measurement of gases is difficult, mainly due to unknown gas density. 

Selective adaptation for a gas has a better chance of success if the objective 

is merely gas detection, since there are less stringent requirements for 

quantitative accuracy. In that case sensitivity would be more important, and 

some gases with very high absorption might still be measurable. 



Spectral adaptation could also be applied the opposite way, i.e., selection of a 

spectral band where the transmission through a medium is as high as 

possible. The purpose would be to enable measurement on an object by 

seeing through the medium without any interference. The medium could be 

ordinary atmosphere, the atmosphere of combustion gases inside a furnace, 

or simply a window (or other solid) through which one wants to measure. 



Filter Types 

The simplest filters are broadband neutral density types that are used merely 

to reduce optical transmission and prevent detector saturation at high 

temperatures. While necessary sometimes, this is not spectral adaptation. In 

spectral adaptation, filters are used in order to suppress or transmit certain 

wavelengths. For discussion purposes, filters can be described as short-pass 

(SP), long-pass (LP), band-pass (BP), and narrow band-pass (NBP). See 

Figure 4.2. SP and LP filters are specified with a cut-on and a cut-off 

wavelength. BP and NBP filters are specified with a center wavelength and a 

half-width (half-power) wavelength, the latter being the width where spectral 

response has decreased to 50% of its maximum. For temperature 

measurements on transparent materials, the filter selected must provide a 

band of essentially complete absorption. Incomplete absorption can be used, 

at least theoretically, provided that both absorptance and reflectance are 

known and stable at the absorption band. Unfortunately, absorption often 

varies with both temperature and thickness of the material. 



Figure 4.2. Response curves for different types of filters 



An example of applying a NBP filter to the measurement of polyethylene film 

temperature is shown in Figure 4.3. The blue curve in the figure shows the 

absorption band of polyethylene film. The red curve shows the transmittance 

of a 3.45μm NBP filter, which is designed to match polyethylene film. The 

green curve shows the resulting transmission through film plus the filter. This 

curve, running just above the zero line, indicates an excellent filter adaptation, 

i.e. the film appears to be opaque to the camera, and no background radiation 

would disturb the measurement of film temperature. Filters can also be 

classified according to their application temperature. Traditionally, cold filters, 

filters that are stabilized at or near the same temperature as the detector, are 

the most accurate and desired filters for thermal signatures. Warm filters, 

filters screwed onto the back of the optical lens outside of the detector/cooler 

assembly, are also commonly used but tend to provide more radiometric 

calibration uncertainty due to varying IR emission with ambient temperature 

changes. Once a filter is selected for use with a particular camera, the 

camera/filter combination needs to be calibrated by the camera manufacturer. 

Then the performance of the system should be characterized since accuracy 

and sensitivity will be affected due to a reduction in energy going to the 

detector. 



Figure 4.3. Application of an NBP filter to achieve nearly complete absorption 

and high emittance from polyethylene film, allowing its temperature 

measurement 



Transparent Material Measurement Techniques 

Production of sheet glass and thin plastic film requires fairly tight temperature 

control to maximize production quality and yield. Traditionally, temperature 

sensors have been embedded at the orifice of the extruder, which provides 

rather coarse information about sheet/film temperature. An IR machine vision 

system can make noncontact temperature measurements and supply more 

usable data about the material as it is extruded. However, as described above, 

an appropriate filter is needed for the IR camera to make the material appear 

opaque. To ensure that the proper filter was selected, spectral response 

curves for the camera/filter system can be created by the camera 

manufacturer. (See the green curve in Figure 4.3.) In fact, this is generally 

required for permanent cold filter installations to validate filter response. 

Otherwise, (with supportive spectral data) the user can proceed by checking 

emissivity. This is a verification of emissivity efficiency for the overall system 

response, including the target material and camera with installed filter. 



Recalling Kirchhoff’s law, 

ρ λ + Ɛ λ + τ λ = 1, or Ɛ λ = 1 – τ λ 

– ρ λ 

it is clear that in order to get an emissivity value Ɛ, transmittance τ and 

reflectance ρ at the pass band of the filter must be known. The transmittance, 

τ λ , can be taken directly from a transmission diagram like the one in Figure 3 

(a value of about 0.02 in that example). 

Where: 

absorbtion alpha (α), 

reflection Rho (ρ), 

transmission Tau (τ ), 

emissivity epsilon (Ɛ). 



Reflectance is less easy to characterize and usually is a function of material 

thickness. However, a transmission diagram like the one in Figure 4.4 

provides some indication of this parameter’s value. Using the blue curve for 

the thinnest polyethylene material in Figure 4, which has the lowest 

absorption, the transmission between absorption bands is seen to be 

approximately 90%. If there were no absorption bands at all, we could 

conclude that the reflection would be 10%. Since there are some narrow 

absorption bands under the curve, we can estimate the reflection to be 8% in 

the spectral regions where absorption is very low. However, we are interested 

in the reflectance where the absorption is high (i.e., where the material 

appears to be opaque). To estimate the reflectance of this polyethylene film, 

we must first make the reasonable assumption that its surface reflectance 

stays constant over the absorption bands. Now recognize that the 8% value is 

the result of reflections from both sides of the film, i.e., approximately 4% per 

surface. 



Figure 4.4. Transmission bands for polyethylene films of three different 

thicknesses 



At the absorption band, however, since the absorption in the material is 

almost complete, we get reflection only on one side. Thus ρ λ = 0.04. 

From this ρ λ , and the τ λ value obtained from the transmission graph (Figure 3 

in this example), emissivity can be calculated: Ɛ λ = 1 – 0.02 – 0.04 = 0.94. 

This value is entered into the camera’s measurement database before having 

it calculate the temperatures from radiance observations. Sheet and plate 

glass production have similar temperature measurement requirements. The 

most common industrial varieties are variations of soda-lime- silica glass. 

Although they may vary in composition and color, their spectral characteristics 

do not change much. Looking at the spectral transmittance of such a glass 

with different thicknesses (Figure 4.5), one can conclude that IR temperature 

measurement must be restricted to wavelengths above 4.3μm. Depending on 

glass thickness, this may require either a mid wavelength (MW) or long 

wavelength (LW) camera/detector. MW cameras cover some portion of the 

spectrum from 2μm – 5μm, and LW cameras cover some portion within 8μm 

–12μm. 



Figure 4.5. Transmission curves for a common industrial glass in five 

thicknesses from 0.23mm to 5.9mm 



In selecting a filter, the temptation might be to go for an LP type with a cut-on 

wavelength near the point where transmittance drops to zero. However, there 

are other factors to consider. For example, LP filter characteristics can 

interfere with the negative slope of the spectral response curve of thermolectrically 

cooled HgCdTe (MCT) detectors, which are used in both MW and 

LW cameras. A better choice may be a NBP filter. In Figure 4.6, transmission 

characteristics of a glass, an SW camera, and two filters are superimposed. 

The green curve represents the LP filter response curve, whereas the NBP 

filter response is shown in blue. The latter was selected for the spectral 

location where glass becomes “black,” and has a center wavelength of 5.0μm. 



Figure 4.6. Two alternative filters for glass measurement with a SW camera 



The reflectance of this glass is shown in Figure 4.7. Note the peak between 8 

and 12 μm, which must be avoided when using an LW camera to measure 

the glass. Another consideration is the camera’s viewing angle, because 

glass reflectance can change with angle of incidence. Fortunately reflectance 

does not change much up to an angle of about 45° relative to normal 

incidence (Figure 4.8). From Figure 4.8, a value 0.025 for the glass 

reflectance is valid when using either the 4.7μm LP or the 5.0μm NBP filter 

(Figure 4.6), because they both operate in the 5μm region. Consequently a 

proper value for the glass emissivity in those cases would be 1 – 0.025 = 

0.975. 



Figure 4.7. Reflectance of a common glass at normal (perpendicular) 

incidence 



Figure 4.8. Glass reflectance as a function of camera viewing angle relative 

to normal incidence 



Transmission Band Applications 

For many applications, the user will need to find a spectral band where the 

medium through which the camera is looking has minimum influence on the 

measurement. The object of interest is at the end of a measurement path on 

the other side of the medium. The medium is in most cases ordinary 

atmosphere, but it could also be a gas or a mixture of gases (e.g., combustion 

gases or flames), a window, or a solid semitransparent material. As is the 

case in absorption band applications, a spectral transmission measurement 

of the actual medium would be the ideal starting point. The objective is to find 

a band within the camera’s response curve where the medium has minimum 

influence on IR transmission from the target object. However, it is often 

impractical to perform such a measurement, particularly for gases at high 

temperatures. In such cases it may be possible to find the spectral properties 

of gas constituents (or other media) in IR literature, revealing a suitable 

spectrum for the measurement. 



In most cases, IR camera manufacturers have anticipated the atmospheric 

attenuation problem. Camera manufacturers typically add a filter that reduces 

measurement errors due to inaccurate and/or varying atmospheric 

parameters by avoiding absorption bands of the constituent gases and water 

vapors. This is especially needed at long measurement distances and shorter 

wavelengths. For MW cameras, an appropriate filter utilizes the atmospheric 

window between the absorption bands of H 2 O+CO 2 around 3μm or CO 2 at 

4.2μm. Atmospheric effects on an LW camera are much less, since the 

atmosphere has an excellent window from 8μm to 12μm. However, cameras 

with a broad response curve reaching into the MW spectrum may require an 

LP filter. This is particularly true for high temperature measurements where 

the radiation is shifted towards shorter wavelengths and atmospheric 

influence increases. An LP filter with a cut-on at 7.4 μm blocks the lower part 

of the camera’s response curve. 



An interesting transmission band application is temperature measurements 

on a gas-fired furnace, oven, or similar heating equipment. Objectives could 

be the measurement of flame temperature or the measurement of internal 

components through the flames. In the latter case, an unfiltered IR camera 

will be overwhelmed by the intense radiation from the flames, making 

measurement of the much weaker radiation from internal objects impossible. 

On the other hand, any transmission through the flames from cooler internal 

objects will make flame temperature measurements inaccurate. The flame 

absorption spectrum in Figure 4.9 reveals the spectral regions where these 

two types of measurement could be made. There is very little radiation from 

the flames in the 3.9 μm area, whereas there is a lot of radiation between the 

4.2 and 4.4μm range. The idea is to employ filters that utilize these spectral 

windows for the desired measurements. 



Figure 4.9. Flame absorption spectrum of a gas-fired furnace with two types 

for filters for different measurement applications 



For measurement of internal components, you need to avoid strong 

absorption bands because they attenuate the radiation from the target object 

and they emit intensely due to the high gas temperature, thus blinding the 

camera. Although gas-fired combustion gases consist mostly of CO 2 and 

water vapor, an atmospheric filter is unsuitable because gas concentrations 

and temperatures are much higher. This makes the absorption bands deeper 

and broader. A flame filter is needed for this application. See Figure 9. This is 

a BP filter transmitting between 3.75 μm and 4.02 μm. With this filter installed, 

the camera will produce an image where the flames are almost invisible and 

the internal structure of the furnace is presented clearly (Figure 10). 

To get the maximum temperature of the flames, a CO 2 filter will show they are 

as high as 1400°C. By comparison, the furnace walls as seen with the flame 

filter are a relatively cool 700°C. 



Figure 4.10. FLIR ThermaCAM?image of furnace tubes with flame filter to 

allow accurate temperature measurement 



Conclusions 

Filters can extend the application of IR cameras into areas that might 

otherwise restrict their use. Still, some preliminary spectrophotometer 

measurements may be needed on the objects and media of interest if spectral 

information cannot be found in IR literature. Once a filter is selected and 

installed, the camera/filter system should be calibrated by the camera 

manufacturer. Even with a well-calibrated system, it is a good idea to avoid 

errors by not using spectral regions of uncertain or varying absorption relative 

to the camera/ filter system response spectrum. 



Infrared Filter 






Chapter 5: Ultra High-Speed Thermography 

Recent Advances in Thermal Imaging 

We have all seen high-speed imagery at some point in our lives, be it a video 

of a missile in flight or a humming bird flapping its wings in slow motion. Both 

scenarios are made possible by high-speed visible cameras with ultra short 

exposure times and triggered strobe lighting to avoid image blur, and usually 

require high frame rates to ensure the captured video plays back smoothly. 

Until recently, the ability to capture high-speed dynamic imagery has not been 

possible with traditional commercial IR cameras. Now, recent advances in IR 

camera technologies, such as fast camera detector readouts and high 

performance electronics, allow high-speed imagery. 



Challenges prohibiting high-speed IR cameras were based primarily on readout electronic 

designs, camera pixel clocks, and backend data acquisition systems being too slow. Older 

readout designs only allowed minimum integration times down to about 10μs, which in 

some cases were insufficient to stop motion on a fast moving target without image blur. 

Similarly, targets with very fast temperature changes could not be sampled at an adequate 

frame rate to accurately characterize the object of interest. Even with the advent of faster 

IR cameras, there still remains the hurdle of how to collect high resolution, high-speed data 

without overwhelming your data collection system and losing frames of data. Not all 

challenges for high-speed IR cameras were due to technology limitations. 

Some were driven by additional requirements that restricted the maximum frame rates 

allowed. For example, cameras that required analog video output naturally restricted the 

maximum frame rate due to the NTSC and PAL format requirements of 30Hz or 25Hz, 

respectively. This is true regardless of the detector focal plane array’s (FPA) pixel rate 

capabilities, because the video monitor’s pixel rates are set by the NTSC or PAL timing 

parameters (vertical and horizontal blanking periods). However, with new improvements in 

high-end commercial R&D camera technologies, all these challenges have been overcome 

and we can begin exploring the many benefits of high-speed IR camera technology. The 

core benefits are the ability to capture fast moving targets without image blur, acquire 

enough data to properly characterize dynamic energy targets, and increase the dynamic 

range without compromising the number of frames per second. 



Reducing Image Blur with Short Integration Times 

With advanced FPA Readout Integrated Circuits (ROIC), IR cameras can 

have integration times (analogous to exposure time or shutter speed in visible 

cameras) as short as 500ns. In addition, new ROIC designs maintain linearity 

all the way to the bottom of their integration time limits; this was not true for 

ROICs developed only a few years ago. The key benefit again is to avoid 

motion blur as the target moves or vibrates through the field of view of the 

camera. With sub-microsecond integration times, these new cameras are 

more than sufficient for fast moving targets such as missiles or in the 

following example, a bullet in flight. 



Faster Than a Speeding Bullet 

In the following experiment, a high speed IR Camera was used to capture and 

measure the temperature of a 0.30 caliber rifle bullet in flight. At the point of image 

capture the bullet was traveling at supersonic speeds (800–900 meters per second) 

and was heated by friction within the rifle barrel, the propellant charge, and 

aerodynamic forces on the bullet. Due to this heat load, the IR camera could easily 

see the bullet even at the very short integration time of 1μs; so unlike a visible camera, 

no strobe source is needed. A trigger was needed to start the camera integration time 

to ensure the bullet was in the Field of View (FOV) of the camera at the time of frame 

capture. This was done by using an acoustic trigger from the rifle shot, which locates 

the bullet along the axis of fire to within a distance of several centimeters. Figure 5.1a 

shows a close-up IR image of the bullet traveling at 840m/s (~ 1900 mph); yet using 

the 1μs integration time, effectively reduced the image blur to about 5 pixels. Figure 

5.1b shows a reference image of an identical bullet imaged with a visible light camera 

set to operate with a 2-microsecond integration time. 

The orientation of the bullets in the two images is identical – they both travel from left 

to right. The bright glow seen on the waist of the image is a reflection of bright studio 

lights that were required to properly illuminate the bullet during the exposure. Unlike 

the thermal image, the visible image required active illumination, since the bullet was 

not hot enough to glow brightly in the visible region of the spectrum. 



Figure 5.1a. Infrared image of a 0.30 caliber bullet in flight with apparent 

temperatures 



Figure 5.1b. Visible-light image of an identical 0.30 caliber bullet in flight 



High-Speed Imaging for Fast Transients 

Short integration times and high-speed frame rates are not always paired 

together in IR cameras. Many cameras have fast frame rates but not fast 

integration times or vice versa. Still, fast frame rates are critical for properly 

characterizing targets whose temperatures change very quickly. An 

application where both short integration time and fast frame rate are required 

is overload testing of integrated circuits (ICs). See Figure 5.2. The objective of 

this test is to monitor the maximum heat load the IC experiences when biased 

and reverse biased with current levels outside the design limits. Without highpeed 

IR technology, sufficient data might not be captured to characterize the 

true heat transients on the IC due to under sampling. This would not only give 

minimal data to analyze, but could also give incorrect readings of the true 

maximum temperature. When the IC was sampled at a frame rate of 1000Hz, 

a maximum temperature of 95°C was reported. However, when sampled at 

only 500Hz, the true maximum temperature was missed and a false 

maximum of 80°C was reported (Figure 5.3). This is just one example of why 

high-speed IR cameras can be so valuable for even simple applications that 

don’t necessarily appear to benefit from high speed at first consideration. 



Figure 5.2. Integrated circuit with 800ms overcurrent pulse 



Figure 5.3. Maximum IC temperature data – actual vs. undersampled 



Pixel Clock vs. Analog to Digital Taps 

High-speed IR cameras require as a prerequisite a combination of a fast pixel 

clock and a higher number of analog to digital (A/D) converters, commonly 

called channels or taps. As a frame of reference, most low performance 

cameras have two channels or A/D converters and run at lower than 40 

megapixels/second clock rates. This may sound fast, but when you consider 

the amount of data, that translates into around 60Hz in most cases. Highpeed 

IR cameras on the other hand typically have a minimum of four 

channels and have clock speeds of at least 50 megapixels/second. In turn 

they offer 14-bit digital data at frame rates of over 120Hz at 640 × 512 

window sizes. In order to increase frame rates further, IR cameras usually 

allow the user to reduce the window size or number of pixels read out from 

FPA. Since there is less data per frame to digitize and transfer, the overall 

frame rate increases. Figure 5.4 illustrates the increase in frame rates relative 

to user defined window sizes. Newer camera designs offer 16 channels and 

pixel clocks upwards of 205 mega pixels/second. This allows for very fast 

frame rates without sacrificing the window size and overall resolution. 



Figure 5.4. Example of FPA window sizes relative to frame rates 



Preset Sequencing Increases Dynamic Range 

High-speed IR cameras have an additional benefit that does not relate to 

highspeed targets, but rather to increasing the dynamic range of the camera. 

By coupling a high-speed IR camera with a data capture method known as 

superframing, you can effectively increase the camera’s dynamic range from 

14 bits to around 18 bits – 22 bits per frame. Superframing involves cycling 

the IR camera through up to four multiple integration times (presets), 

capturing one frame at each preset. This results in multiple unique data movie 

files, one for each preset. This data is then combined by using off-the-shelf 

ABATER software. The software selects the best resolved pixel from each 

unique frame to build a resultant frame composed of data from all the 

collected data movie files at varying integration times. This method is 

especially beneficial for those imaging scenes with both hot and cold objects 

in the same field of view. Typically a 14-bit camera cannot image 

simultaneously both hot and cold objects with a single integration time. This 

would result in either over exposure on the hot object or under exposure on 

the cold object. The results of superframing are illustrated in the Beechcraft 

King Air aircraft images in Figure 5.5, captured at two different integration 

times. 



Figure 5.5. Active aircraft engine imaged at integration rates of 2ms (left) and 

30μs (right) 



While the aircraft can be clearly seen in the left image (Preset 0 = 2ms 

integration time), there are portions of the engine that are clearly over 

exposed. Conversely, the right image in Figure 5.5 (Preset 1 = 30μs 

integration time), shows engine intake and exhaust detail with the remainder 

of the aircraft underexposed. When the two images in Figure 5.5 are 

processed in ABATER software, the best resolved pixels are selected and 

used to build a single resultant superframed image with no over or under 

exposed pixels (Figure 5.6). As you may have figured out, the down side to 

this method of data collection and analysis is the reduction in the frame rate 

by the number of Presets cycled. By applying some simple calculations a 

100Hz camera with two Presets will provide an overall frame rate of 50Hz, 

well under the limits of our discussion of high speed IR imagery. This only 

reinforces the need for a high speed camera. If a 305Hz camera is 

superframed as in the example above, a rate of over 150Hz per preset frame 

rate is achieved. This rate is well within the bounds of high speed IR imaging. 



Figure 5.6. Superframed image created with ABATER software from Preset 0 

and Preset 1 data. 



Conclusions 

Sophisticated IR cameras are now available with advanced readout 

electronics and high speed pixel clocks, which open the door for high speed 

IR imagery. This allows us to expand the boundaries of which applications 

can be solved using IR camera solutions. Furthermore, it allows us to begin 

capturing more data and increase our accuracy for demanding applications 

with fast moving targets, quick temperature transients, and wide dynamic 

range scenes. With the release of this new technology in the commercial IR 

marketplace, we can now begin to realize the benefits of high speed data 

capture, once only available to the visible camera realm. 



A wide range of thermal imaging cameras for R&D and Science 

applications 

FLIR markets a full product range of thermal imaging cameras for R&D 

applications. Whether you are just discovering the benefits that thermal 

imaging cameras have to offer or if you are an expert thermographer, FLIR 

offers you the correct tool for the job. Discover our full product range and find 

out why FLIR is the world leader for thermal imaging cameras. 





Software for demanding thermal imaging professionals 

At FLIR, we recognize that our job is to go beyond just producing the best 

possible infrared camera systems. We are committed to enabling all users of 

our thermal imaging camera systems to work more efficiently and productively 

by providing them with the most professional camera-software combination. 

Our team of committed specialists are constantly developing new, better and 

more user-friendly software packages to satisfy the most demanding thermal 

imaging professionals. All software is Windows-based, allows fast, detailed 

and accurate analysis and evaluation of thermal inspections. 

For more information on FLIR products or software please contact your 

nearest FLIR representative of visit www.flir.com 




MWIR for Remote Sniffing and Locating of Gases 



A mid-wave infrared camera visualizes methane gas leaking from a 

drilling platform. 

Methane is a greenhouse gas that is significantly more damaging than 

carbon dioxide. When it leaks uncontrolled from industrial plants, it can easily 

escape into the atmosphere. This is seen primarily as an economic loss, but it 

also contributes to the destruction of the environment. Sensitive highresolution 

mid-wave infrared (MWIR) cameras can accurately detect such 

emissions without placing a direct-measurement gas-detection system in the 

danger zone. 

Raf Vandersmissen, Sinfrared, and Thomas Zimmermann, TIB Infrared 

Solutions 



Figure 1. The transmission spectrum of methane (CH4) shows a strong 

attenuation between 3.3 and 3.6 µm and at a wavelength of 7.6 µm. 



Gas leaking from a drilling platform 

A March 2012 incident on the Elgin Wellhead gas drilling platform in the 

North Sea focused public attention on the risk connected with offshore mining 

of oil and gas.1 Elgin Wellhead (Figure 2) is one of more than 400 rigs in the 

North Sea, and it is a significant part of the exploration of the Elgin/Franklin 

field. As was discovered later, a gas leak had formed at the cover of well G4, 

which had been installed a few months earlier. A sequence and combination 

of several individual events never seen before had led to a kind of stress 

corrosion, which occurred only on G4. The incident was caused mainly by a 

chalk layer about 1000 m above the gas reservoir. 



Figure 2. The 

Elgin Wellhead 

drilling platform is 

one of more than 

400 rigs located in 

the North Sea. 


The Elgin Wellhead drilling platform is one of more than 400 rigs located in 
















http://www.theoildrum.com/node/9072


http://www.theoildrum.com/node/9072

As a consequence, a severe methane gas leak developed in this area. 

Production was halted, and the drilling platform crew was evacuated, 

because the exact position of the leak and the amount of gas emitted could 

not be determined. Additionally, it was not known whether the flame on top of 

the platform was still burning. Because of the danger of a possible explosion, 

a three-mile no-go area surrounding the rig was declared, and another 4000-ft 

exclusion zone was established in the airspace above the rig. 

However, these precautions complicated the initial efforts to close the leak, 

as appropriate countermeasures could be attempted only after the gas 

concentration at the accident location had fallen below a critical value. Since 

these exposure limits did not permit taking a direct gas probe and subsequent 

gas analysis, an alternative method had to be considered to determine the 

methane content in the environment from afar. It was found in an absorption 

measurement of the infrared light in hydrocarbons. 



IR absorption of hydrocarbons 

Hydrocarbons are chemical compounds that consist only of carbon and 

hydrogen. Their simplest configuration is methane, a colorless and odorless 

combustible gas having the notation CH4. Its molecular model is shown in 

Figure 1.2 Its four C-H bonds are pointing toward the corners of a 

tetrahedron.This molecule structure exhibits four atomic oscillation modes, 

differentiated by either generating a temporal dipole moment – modes (a) and 

(d) – or not – modes (b) and (c). Only the first two can be excited by an 

external infrared radiation. They have a transmission spectrum shown in 

Figure 1 for the wave number area reaching from 4000/cm (which relates to a 

wavelength of 2.5 µm) to 500/cm (20 µm). 

A wave number of 3019/cm (3.3-µm wavelength) relates to the absorption 

band of the antisymmetrical valence oscillation (a), whereas the deformation 

oscillation (d), whose movement is similar to the unfolding of an umbrella, 

causes a strong attenuation around a wavelength of 7.6 µm (at a wave 

number of 1306/cm). Both areas are suited for remote methane gas sniffing, 

with the stronger attenuation value favoring the use of the MWIR band around 

3.3 µm. Additionally, it would be advantageous if the image components in 

the visible spectrum were captured as well for a spatial assignment. 



MWIR camera with extended spectrum area 

In the case of the gas leak at the Elgin Wellhead drilling platform, the 

measurement task was defined primarily as visualizing the gas components 

(methane, butane and carbon dioxide), as well as the characteristics of their 

spreading in the form of a gas cloud close to the sea surface and in the 

atmosphere. An additional goal was determining the activity of the flame on the 

platform. These investigations were carried out by TIB Infrared Solutions, in 

cooperation with the laboratory for environmental measurements at the University 

of Applied Sciences Düsseldorf. 

The Onca MWIR camera from Xenics, with expanded spectral coverage, was 

used for this purpose (Figure-3). 

The Onca camera family is equipped with a Stirling-cooled detector array in 

mercury-cadmium-telluride (MCT) or InSb (why or ?) detector technology. It is 

especially well suited for research and development tasks with high-speed image 

capture or IR spectroscopy. The camera operates in the extended MWIR area 

from 3.7 to 4.8 µm, which can be further expanded to cover the 

1 µm - to 5 µm wavelength area. This camera layout increases its all-weather and 

night-vision suitability and is of specific interest for spectroscopy applications. 



Figure 3. The extended spectral area of an MWIR camera simplifies the 

spatial assignment of gas plumes to visible objects. 



Using the TrueThermal technique, this camera system offers an accuracy of 

±2 °C (or ±2 percent, whichever is largest). This is made possible by a new, 

stable method called NUC (nonuniformity correction), which eliminates the 

need for frequent adjustments. Four optional radiometric temperature ranges 

– −20 to 120 °C, 50 to 400 °C, 300 to 1200 °C and 1000 to 2000 °C – cover a 

wide variety of applications. 

The camera platform is optimized for autonomous operation and also in 

combination with a PC. Among other valuable features, it offers a mode for 

real-time image correction. The area-array sensor is available with a standard 

resolution of 320 × 256 pixels (InSb) or 384 × 288 pixels (MCT), or in a highresolution 

version featuring 640 × 512 pixels. Both versions offer the 

sensitivity specification NETD (noise-equivalent temperature difference) of 

less than 20 mK (?) . 



The camera output delivers image 

The camera output delivers image data that is 14 bits wide at various frame rates. 

Two speeds are available: 30-Hz standard video with 640 × 512 pixels, and 60- 

Hz with 320 × 256 (InSb) or 384 × 288 (MCT) pixels. Additionally, 100 Hz is 

available at high resolution, as well as an extremely fast version with 460 Hz and 

320 × 256-pixel resolution. Reading out partial images will further increase the 

frame rate, which is especially useful for process-control applications at high 

frame rates. The camera functions can be tailored to the specific application, with 

the parameters stored in the camera’s nonvolatile memory. 

To adapt the camera to a specific measurement procedure, five filter-position 

options have been incorporated. Unlike most filter holders, which accept just one 

25-mm filter of 1-mm thickness, the holders on the Onca’s filter wheel are much 

deeper, and each position can hold several filters up to a total thickness of 3 mm 

for spectral measurements. 

The camera is user-programmable to offer individual filter positions and 

integration times for each exposure. However, because of the additional time 

needed for changing the filter and readjusting the operational parameters, the 

maximum frame rate mentioned above cannot always be reached. 



This flexibility, together with a stable thermal calibration across various 

integration times, enables a new operational mode called SuperFraming, 

which delivers thermal images of a high dynamic range in real time derived 

from images specifically exposed to users’ specifications. Using this special 

operational mode, the user can locate hot and cold spots in an image for fast 

process-monitoring sequences. 

Camera control and image capture are laid out for more than 100 images per 

second. Compatibility with GigE Vision and Camera Link simplifies the 

integration in user systems. For application development support, an API is 

available, as well as sample codes in C++, Visual Basic and Delphi; LabView 

drivers; and a comprehensive DLL library. 



Remote gas measurement 

The solution to the complex measurement task of visualizing gas components 

leaking from the well close to the drilling platform and spreading as a plume 

over the sea surface into the atmosphere was deploying the high-resolution 

(640 × 512 pixels) MWIR camera with an extended wavelength area from 1 

to 5 µm, operating at a frame rate of 100 Hz. Using this type of camera broke 

new ground for the researchers. The exclusion zone forced the IR 

investigation to be done from a large distance. This was an operational 

condition, for which there were no previously established procedures or 

proven results. 

The methane clouds can be detected even under these difficult measurement 

conditions; this is shown in an image taken from a plane flying over the scene 

(Figure 4). In the upper right is the drilling platform, with flame burning. The 

shadows visible in the foreground and in the center are caused by the 

methane gas plumes. They are illuminated by light reflections on the sea 

surface, and they absorb the midrange IR radiation at around 3.3 µm. 



This image-capture process requires two filters, one for the bandpass around 3.3 

µm, for methane absorption, and a second in the short-wave IR to make physical 

objects such as the drilling platform visible. By analyzing this dual-band image, 

certain gas concentrations can be assigned to fixed objects. This makes the 

interpretation of such images much easier. To detect gases whose transmission 

spectrum is different from that of methane, other filters or filter combinations are 

available. The results can be presented in different colors to distinguish among 

several gases in one image. 

Taking aerial pictures of the gas-leak scene was not the only investigative 

method for determining the scope of the disturbance at the drilling platform. 

Examinations were also carried out from aboard a ship (Figure 5). They proved 

that the water molecules and drops suspended in the atmosphere above the sea 

level are causing a strong attenuation of the MWIR radiation. Despite this effect, 

the gas components located behind this water vapor can be determined because 

naturally occurring clouds appear fully saturated, whereas the gas plume appears 

more like a fog. Deploying the MWIR camera allowed a quick check of the 

working conditions so that a repair crew could be sent to the site. On May 15, the 

leak was plugged by discharging heavy mud into the well. After final closure with 

five cement plugs in October 2012, the drilling platform went back to operation in 

March 2013. 



Figure 4. From above, methane plumes look like dark clouds in the MWIR 

spectrum. 



Figure 5. The high-resolution, high-sensitivity MWIR camera Onca performs 

a checkup from sea level. 



Figure 6. False-color representation of MWIR images enables the detection 

of the gas plume behind clouds formed by water vapor. 



False-color representation of MWIR images enables the detection of the gas 

plume behind clouds formed by water vapor. 



False-color representation of MWIR images enables the detection of the gas 

plume behind clouds formed by water vapor. 



Gas leaking from a drilling platform 

http://www.dailymail.co.uk/sciencetech/article-2125471/Total-gas-leak-Greenpeace-release-infra-red-image-explosive-gas-spewing-Elgin-rig.html#v-1535918156001 



FLIR GF-Series Infrared Cameras for Safe Gas Detection 

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many oil and gas companies. It's a proactive way to identify sources of Volatile Organic Compound (VOC) emissions and repair 

leaking components before it's too late. By using the most advanced VOC detection, you will improve safety and productivity and 

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■ 

http://www.flir.co.uk/ogi/display/?id=49559 



What is Emissivity 


http://www.optotherm.com/emiss-physics.htm

More Reading: What is 

Emissivity 

All objects and materials do not 

radiate infrared (thermal) energy 

equally. Emissivity is a term 

describing the efficiency with which a 

material radiates infrared energy. A 

blackbody has an emissivity of 1.00 

and no other material can radiate 

more thermal energy at a given 

temperature. An object with an 

emissivity of 0 emits no infrared 

energy. Real-world objects have 

emissivity values between 0 and 

1.00. 

The lower emissivity of most real-world materials reduces the intensity of radiation 

from the theoretical predictions of Planck’s Law. 

The temperature of an object and its emissivity define how much infrared energy an 

object will emit. The figure below shows that quartz emits less energy than a 

blackbody at the same temperature and therefore has an emissivity below 1.00. 



Physics of Infrared’s Emissivity 

Infrared (thermal) energy, when incident upon matter, be it solid, liquid or gas, will exhibit the properties of 

absorption σ, reflection ρ, and transmission τ to varying degrees. 

τ 

Absorption σ 

Absorption is the degree to which infrared energy is absorbed by a material. Materials such as plastic, ceramic, 

and textiles are good absorbers. Thermal energy absorbed by real-world objects is generally retransferred to 

their surroundings by conduction, convection, or radiation. 

Transmission τ 

Transmission is the degree to which thermal energy passes through a material. There are few materials that 

transmit energy efficiently in the infrared region between 7µm and 14µm. Germanium is one of the few good 

transmitters of infrared energy and thus it is used frequently as lens material in thermal imaging systems. 

Reflection ρ 

Reflection is the degree to which infrared energy reflects off a material. Polished metals such as aluminum, 

gold and nickel are very good reflectors. Conservation of energy implies that the amount of incident energy is 

equal to the sum of the absorbed, reflected, and transmitted energy. 

(1) Incident Energy = Absorbed Energy W σ 

+ Transmitted Energy W τ 

+ Reflected Energy W ρ 



Emitted Energy = Absorbed Energy 

Consider equation 1 for an object in a vacuum at a constant temperature. Because it is in 

a vacuum, there are no other sources of energy input to the object or output from the object. 

The absorbed energy by the object increases its thermal energy - the transmitted and 

reflected energy does not. In order for the temperature of the object to remain constant, the 

object must radiate the same amount of energy as it absorbs. 

(2) Emitted Energy = Absorbed Energy 

Therefore, objects that are good absorbers are good emitters and objects that are poor 

absorbers are poor emitters. Applying equation 2, Equation 1 can be restated as follows: 

(3) Incident Energy = Emitted Energy + Transmitted Energy + Reflected Energy 

Setting the incident energy equal to 100%, the equation 3 becomes: 

(4) 100% = %Emitted Energy + %Transmitted Energy + %Reflected Energy 

Because emissivity equals the efficiency with which a material radiates energy, equation 4 

can be restated as follows: 

(5) 100% = Emissivity + %Transmitted Energy + %Reflected Energy 

Applying similar terms to %Transmitted Energy and %Reflected Energy, 

(6) 100% = Emissivity + Transmissivity + Reflectivity 



According to equation 6, there is a balance between emissivity, transmissivity, and reflectivity. 

Increasing the value of one of these parameters requires a decrease in the sum of the other two 

parameters. If the emissivity of an object increases, the sum of its transmissivity and reflectivity 

must decrease. Likewise, if the reflectivity of an object increases, the sum of its emissivity and 

trasmissivity must decrease. 

Most solid objects exhibit very low transmission of infrared energy - the majority of incident 

energy is either absorbed or reflected. By setting transmissivity equal to zero, equation 6 can be 

restated as follows: 

(7) 100% = Emissivity + Reflectivity 

For objects that do not transmit energy, there is a simple balance between emissivity and 

reflectivity. If emissivity increases, reflectivity must decrease. If reflectivity increases, emissivity 

must decrease. For example, a plastic material with emissivity = 0.92 has reflectivity = 0.08. A 

polished aluminum surface with emissivity = 0.12 has reflectivity = 0.88. 

The emissive and reflective behavior of most materials is similar in the visible and infrared 

regions of the electromagnetic spectrum. Polished metals, for example, have low emissivity and 

high reflectivity in both the visible and infrared. It is important to understand, however, that some 

materials that are good absorbers, transmitters, or reflectors in the visible, may exhibit 

completely different characteristics in the infrared. 



Infrared Thermometer Emissivity tables 

http://www.scigiene.com/pdfs/428_InfraredThermometerEmissivitytablesrev.pdf 



Infrared Thermometer Emissivity tables 

Understanding an object's emissivity or its characteristic "radiance" is a critical 

component in the proper handling of infrared measurements. Concisely, emissivity 

is the ratio of radiation emitted by a surface or blackbody and its theoretical 

radiation predicted from Planck's law. [W(L,T)=C1/(L^5*(exp(C2/LT)-1)] A 

material's surface emissivity is measured by the amount of energy emitted when 

the surface is directly observed. There are many variables that affect a specific 

object's emissivity, such as the wavelength of interest, field of view, the geometric 

shape of the blackbody, and temperature. However, for the purposes and 

applications of the infrared thermometer user, a comprehensive table showing the 

emissivity at corresponding temperatures of various surfaces and objects is 

displayed. 

The table below can be used to adjust the emissivity of any of the listed I.R. 

thermometers: 

• TN408LC 

• TN418LD 

• TN40ALC 

• TN425LE 

All have variable emissivity in order to improve the accuracy of the readings. 

For further assistance please contact us at Scigiene Corp. www.scigiene.com 

416-261-4865 and we will be happy to assist. 

Non-Metal Emssivity table 

Material(Non-Metals) Temp degF(degC) Emissivity 

Adobe 68 (20) 0.9 

Asbestos 

Board 100 (38) 0.96 

Cement 32-392 (0-200) 0.96 

Cement, Red 2500 (1371) 0.67 

Cement, White 2500 (1371) 0.65 

Cloth 199 (93) 0.9 

Paper 100-700 (38-371) 0.93 

Slate 68 (20) 0.97 

Asphalt, pavement 100 (38) 0.93 

Asphalt, tar paper 68 (20) 0.93 

Basalt 68 (20) 0.72 

Brick 

Red, rough 70 (21) 0.93 

Gault Cream 2500-5000 (1371-2760) .26-.30 

Fire Clay 2500 (1371) 0.75 

Light Buff 1000 (538) 0.8 

Lime Clay 2500 (1371) 0.43 

Fire Brick 1832 (1000) .75-.80 

Magnesite, Refractory 1832 (1000) 0.38 

Grey Brick 2012 (1100) 0.75 

Silica, Glazed 2000 (1093) 0.88 

Silica, Unglazed 2000 (1093) 0.8 

Sandlime 2500-5000 (1371-2760) .59-.63 

Carborundum 1850 (1010) 0.92


Ceramic 

Alumina on Inconel 800-2000 (427-1093) .69-.45 

Earthenware, Glazed 70 (21) 0.9 

Earthenware, Matte 70 (21) 0.93 

Greens No. 5210-2C 200-750 (93-399) .89-.82 

Coating No. C20A 200-750 (93-399) .73-.67 

Porcelain 72 (22) 0.92 

White Al2O3 200 (93) 0.9 

Zirconia on Inconel 800-2000 (427-1093) .62-.45 

Clay 68 (20) 0.39 

Fired 158 (70) 0.91 

Shale 68 (20) 0.69 

Tiles, Light Red 2500-5000 (1371-2760) .32-.34 

Tiles, Red 2500-5000 (1371-2760) .40-.51 

Tiles,Dark Purple 2500-5000 (1371-2760) 0.78 

Concrete 

Rough 32-2000 (0-1093) 0.94 

Tiles, Natural 2500-5000 (1371-2760) .63-.62 

Brown 2500-5000 (1371-2760) .87-.83 

Black 2500-5000 (1371-2760) .94-.91 

Cotton Cloth 68 (20) 0.77 

Dolomite Lime 68 (20) 0.41 

Emery Corundum 176 (80) 0.86 

Glass 

Convex D 212 (100) 0.8 

Convex D 600 (316) 0.8 

Convex D 932 (500) 0.76 

Nonex 212 (100) 0.82 

Nonex 600 (316) 0.82 

Nonex 932 (500) 0.78 

Smooth 32-200(0-93) .92-.94 

Granite 70 (21) 0.45 

Gravel 100 (38) 0.28 

Gypsum 68 (20) .80-.90 

Ice, Smooth 32 (0) 0.97 

Ice, Rough 32 (0) 0.98 

Lacquer 

Black 200 (93) 0.96 

Blue, on Al Foil 100 (38) 0.78 

Clear, on Al Foil (2 coats) 200 (93) .08-.09 

Clear, on Bright Cu 200 (93) 0.66 

Clear, on Tarnished Cu 200 (93) 0.64 

Red, on Al Foil (2 coats) 100 (38) .60-.74 

White 200 (93) 0.95 

White, on Al Foil (2 coats) 100 (38) .69-.88 

Yellow, on Al Foil (2 coats) 100 (38) .57-.79 

Lime Mortar 100-500 (38-260) .90-.92


Lacquer - continued 

Limestone 100 (38) 0.95 

Marble, White 100 (38) 0.95 

Smooth, White 100 (38) 0.56 

Polished Grey 100 (38) 0.75 

Mica 100 (38) 0.75 

Oil on Nickel 

0.001 Film 72 (22) 0.27 

0.002 " 72 (22) 0.46 

0.005 " 72 (22) 0.72 

Thick " 72 (22) 0.82 

Oil, Linseed 

On Al Foil, uncoated 250 (121) 0.09 

On Al Foil, 1 coat 250 (121) 0.56 

On Al Foil, 2 coats 250 (121) 0.51 

On Polished Iron, .001 Film 100 (38) 0.22 



On Polished Iron, Thick Film 100 (38) 0.83 

Paints 

Blue, Cu2O3 75 (24) 0.94 

Black, CuO 75 (24) 0.96 

Green, Cu2O3 75 (24) 0.92 

Red, Fe2O3 75 (24) 0.91 

White, Al2O3 75 (24) 0.94 

White, Y2O3 75 (24) 0.9 

White, ZnO 75 (24) 0.95 

White, MgCO3 75 (24) 0.91 

White, ZrO2 75 (24) 0.95 

White, ThO2 75 (24) 0.9 

White, MgO 75 (24) 0.91 

White, PbCO3 75 (24) 0.93 

Yellow, PbO 75 (24) 0.9 

Yellow, PbCrO4 75 (24) 0.93 

Paints, Aluminium 100 (38) .27-.67 

10% Al 100 (38) 0.52 

26% Al 100 (38) 0.3 

Dow XP-310 200 (93) 0.22 

Paints, Bronze Low .34-.80 

Gum Varnish (2 coats) 70 (21) 0.53 

Gum Varnish (3 coats) 70 (21) 0.5 

Cellulose Binder (2 coats) 70 (21) 0.34 

Paints, Oil 

All colours 200 (93) .92-.96 

Black 200 (93) 0.92 

Black Gloss 70 (21) 0.9 

Camouflage Green 125 (52) 0.85


Paints, Oil - continued 

Flat Black 80 (27) 0.88 

Flat White 80 (27) 0.91 

Grey-Green 70 (21) 0.95 

Green 200 (93) 0.95 

Lamp Black 209 (98) 0.96 

Red 200 (93) 0.95 

White 200 (93) 0.94 

Quartz, Rough, Fused 70 (21) 0.93 

Glass, 1.98 mm 540 (282) 0.9 

Glass, 1.98 mm 1540 (838) 0.41 

Glass, 6.88 mm 540 (282) 0.93 

Glass, 6.88 mm 1540 (838) 0.47 

Opaque 570 (299) 0.92 

Opaque 1540 (838) 0.68 

Red Lead 212 (100) 0.93 

Rubber, Hard 74 (23) 0.94 

Rubber, Soft, Grey 76 (24) 0.86 

Sand 68 (20) 0.76 

Sandstone 100 (38) 0.67 

Sandstone, Red 100 (38) .60-.83 

Sawdust 68 (20) 0.75 

Shale 68 (20) 0.69 

Silica,Glazed 1832 (1000) 0.85 

Silica, Unglazed 2012 (1100) 0.75 

Silicon Carbide 300-1200 (149-649) .83-.96 

Silk Cloth 68 (20) 0.78 

Slate 100 (38) .67-.80 

Snow, Fine Particles 20 (-7) 0.82 

Snow, Granular 18 (-8) 0.89 

Soil 

Surface 100 (38) 0.38 

Black Loam 68 (20) 0.66 

Plowed Field 68 (20) 0.38 

Soot 

Acetylene 75 (24) 0.97 

Camphor 75 (24) 0.94 

Candle 250 (121) 0.95 

Coal 68 (20) 0.95 

Stonework 100 (38) 0.93 

Water 100 (38) 0.67 

Waterglass 68 (20) 0.96 

Wood Low .80-.90 

Beech Planed 158 (70) 0.94 

Oak, Planed 100 (38) 0.91 

Spruce, Sanded 100 (38) 0.89

Metal Emssivity table 

Material(metal) Temp degF (degC) Emissivity 

Alloys 

20-Ni, 24-CR, 55-FE, Oxid. 392 (200) 0.9 

20-Ni, 24-CR, 55-FE, Oxid. 932(500) 0.97 

60-Ni, 12-CR, 28-FE, Oxid. 518 (270) 0.89 

60-Ni, 12-CR, 28-FE, Oxid. 1040 (560) 0.82 

80-Ni, 20-CR, Oxidised 212 (100) 0.87 

80-Ni, 20-CR, Oxidised 1112 (600) 0.87 

80-Ni, 20-CR, Oxidised 2372 (1300) 0.89 

Aluminium 

Unoxidised 77 (25) 0.02 

Unoxidised 212 (100) 0.03 

Unoxidised 932 (500) 0.06 

Oxidised 390 (199) 0.11 

Oxidised 1110 (599) 0.19 

Oxidised at 599degC(1110degF) 390 (199) 0.11 

Oxidised at 599degC(1110degF) 1110 (599) 0.19 

Heavily Oxidised 200 (93) 0.2 

Heavily Oxidised 940 (504) 0.31 

Highly Polished 212 (100) 0.09 

Roughly Polished 212 (100) 0.18 

Commercial Sheet 212 (100) 0.09 

Highly Polished Plate 440 (227) 0.04 

Highly Polished Plate 1070 (577) 0.06 

Bright Rolled Plate 338 (170) 0.04 

Bright Rolled Plate 932 (500) 0.05 

Alloy A3003, Oxidised 600 (316) 0.4 

Alloy A3003, Oxidised 900 (482) 0.4 

Alloy 1100-0 200-800 (93-427) 0.05 

Alloy 24ST 75 (24) 0.09 

Alloy 24ST, Polished 75 (24) 0.09 

Alloy 75ST 75 (24) 0.11 

Alloy 75ST, Polished 75 (24) 0.08 

Bismuth, Bright 176 (80) 0.34 

Bismuth, Unoxidised 77 (25) 0.05 

Bismuth, Unoxidised 212 (100) 0.06 

Brass 

73% Cu, 27% Zn, Polished 476 (247) 0.03 

73% Cu, 27% Zn, Polished 674 (357) 0.03 

62% Cu, 37% Zn, Polished 494 (257) 0.03 

62% Cu, 37% Zn, Polished 710 (377) 0.04 

83% Cu, 17% Zn, Polished 530 (277) 0.03 

Matte 68 (20) 0.07 

Burnished to Brown Colour 68 (20) 0.4 

Cu-Zn, Brass Oxidised 392 (200) 0.61 

Cu-Zn, Brass Oxidised 752 (400) 0.6 

Cu-Zn, Brass Oxidised 1112 (600) 0.61


Brass - continued 


Unoxidised 212 (100) 0.04 

Cadmium 77 (25) 0.02 

Carbon 

Lampblack 77 (25) 0.95 


Unoxidised 212 (100) 0.81 

Unoxidised 932 (500) 0.79 

Candle Soot 250 (121) 0.95 

Filament 500 (260) 0.95 

Graphitized 212 (100) 0.76 



Chromium 100 (38) 0.08 

Chromium 1000 (538) 0.26 

Chromium, Polished 302 (150) 0.06 

Cobalt, Unoxidised 932 (500) 0.13 

Cobalt, Unoxidised 1832 (1000) 0.23 

Columbium, Unoxidised 1500 (816) 0.19 

Columbium, Unoxidised 2000 (1093) 0.24 

Copper 

Cuprous Oxide 100 (38) 0.87 



Black, Oxidised 100 (38) 0.78 

Etched 100 (38) 0.09 

Matte 100 (38) 0.22 

Roughly Polished 100 (38) 0.07 

Polished 100 (38) 0.03 

Highly Polished 100 (38) 0.02 

Rolled 100 (38) 0.64 

Rough 100 (38) 0.74 

Molten 1000 (538) 0.15 

Molten 1970 (1077) 0.16 

Molten 2230 (1221) 0.13 

Nickel Plated 100-500 (38-260) 0.37 

Dow Metal 0.4-600 (-18-316) 0.15 

Gold 

Enamel 212 (100) 0.37 

Plate (.0001) ·· ·· 

Plate on .0005 Silver 200-750 (93-399) .11-.14 

Plate on .0005 Nickel 200-750 (93-399) .07-.09 

Polished 100-500 (38-260) 0.02 

Polished 1000-2000 (538-1093) 0.03 

Haynes Alloy C, 

Oxidised 600-2000 (316-1093) .90-.96


Haynes Alloy 25, 

Oxidised 600-2000 (316-1093) .86-.89 

Haynes Alloy X, 

Oxidised 600-2000 (316-1093) .85-.88 

Inconel Sheet 1000 (538) 0.28 



Inconel X, Polished 75 (24) 0.19 

Inconel B, Polished 75 (24) 0.21 

Iron 

Oxidised 212 (100) 0.74 

Oxidised 930 (499) 0.84 

Oxidised 2190 (1199) 0.89 

Unoxidised 212 (100) 0.05 

Red Rust 77 (25) 0.7 

Rusted 77 (25) 0.65 

Liquid 2760-3220 (1516-1771) .42-.45 

Cast Iron 

Oxidised 390 (199) 0.64 

Oxidised 1110 (599) 0.78 

Unoxidised 212 (100) 0.21 

Strong Oxidation 40 (104) 0.95 

Strong Oxidation 482 (250) 0.95 

Liquid 2795 (1535) 0.29 

Wrought Iron 

Dull 77 (25) 0.94 

Dull 660 (349) 0.94 

Smooth 100 (38) 0.35 

Polished 100 (38) 0.28 

Lead 

Polished 100-500 (38-260) .06-.08 

Rough 100 (38) 0.43 

Oxidised 100 (38) 0.43 

Oxidised at 1100 100 (38) 0.63 

Gray Oxidised 100 (38) 0.28 

Magnesium 100-500 (38-260) .07-.13 

Magnesium Oxide 1880-3140 (1027-1727) .16-.20 

Mercury 32 (0) 0.09 

Mercury 77 (25) 0.1 

Mercury 100 (38) 0.1 

Mercury 212 (100) 0.12 

Molybdenum 100 (38) 0.06 

Molybdenum 500 (260) 0.08 

Molybdenum 1000 (538) 0.11 

Molybdenum 2000 (1093) 0.18 

Molybdenum Oxidised at 1000degF 600 (316) 0.8 

Molybdenum Oxidised at 1000degF 700 (371) 0.84


Lead - continued 




Monel, Ni-Cu 392 (200) 0.41 

Monel, Ni-Cu 752 (400) 0.44 

Monel, Ni-Cu 1112 (600) 0.46 

Monel, Ni-Cu Oxidised 68 (20) 0.43 

Monel, Ni-Cu Oxid. at 1110degF 1110 (599) 0.46 

Nickel 

Polished 100 (38) 0.05 

Oxidised 100-500 (38-260) .31-.46 


Unoxidised 212 (100) 0.06 

Unoxidised 932 (500) 0.12 

Unoxidised 1832 (1000) 0.19 

Electrolytic 100 (38) 0.04 




Nickel Oxide 1000-2000 (538-1093) .59-.86 

Palladium Plate (.00005 on .0005 silver) 200-750 (93-399) .16-.17 

Platinum 100 (38) 0.05 

Platinum 500 (260) 0.05 

Platinum 1000 (538) 0.1 

Platinum, Black 100 (38) 0.93 



Platinum Oxidised at 1100 500 (260) 0.07 

Platinum Oxidised at 1100 1000 (538) 0.11 

Rhodium Flash (0.0002 on 0.0005 Ni) 200-700 (93-371) .10-.18 

Silver 

Plate (0.0005 on Ni) 200-700 (93-371) .06-.07 

Polished 100 (38) 0.01 

Polished 500 (260) 0.02 

Polished 1000 (538) 0.03 

Polished 2000 (1093) 0.03 

Steel 

Cold Rolled 200 (93) .75-.85 

Ground Sheet 1720-2010 (938-1099) .55-.61 

Polished Sheet 100 (38) 0.07 



Mild Steel, Polished 75 (24) 0.1 

Mild Steel, Smooth 75 (24) 0.12 

Mild Steel,liquid 2910-3270 (1599-1793) 0.28 

Steel, Unoxidised 212 (100) 0.08


Steel - continued 

Steel, Oxidised 77 (25) 0.8 

Steel Alloys 

Type 301, Polished 75 (24) 0.27 

Type 301, Polished 450 (232) 0.57 

Type 301, Polished 1740 (949) 0.55 

Type 303, Oxidised 600-2000 (316-1093) .74-.87 

Type 310, Rolled 1500-2100 (816-1149) .56-.81 

Type 316, Polished 75 (24) 0.28 

Type 316, Polished 450 (232) 0.57 

Type 316, Polished 1740 (949) 0.66 

Type 321 200-800 (93-427) .27-.32 

Type 321 Polished 300-1500 (149-815) .18-.49 

Type 321 w/BK Oxide 200-800 (93-427) .66-.76 

Type 347, Oxidised 600-2000 (316-1093) .87-.91 

Type 350 200-800 (93-427) .18-.27 

Type 350 Polished 300-1800 (149-982) .11-.35 

Type 446, Polished 300-1500 (149-815) .15-.37 

Type 17-7 PH 200-600 (93-316) .44-.51 

Type 17-7 PH Polished 300-1500 (149-815) .09-.16 

Type C1020,Oxidised 600-2000 (316-1093) .87-.91 

Type PH-15-7 MO 300-1200 (149-649) .07-.19 

Stellite, Polished 68 (20) 0.18 

Tantalum, Unoxidised 1340 (727) 0.14 




Tin, Unoxidised 77 (25) 0.04 

Tin, Unoxidised 212 (100) 0.05 

Tinned Iron, Bright 76 (24) 0.05 

Tinned Iron, Bright 212 (100) 0.08 

Titanium 

Alloy C110M,Polished 300-1200 (149-649) .08-.19 

Oxidised at 538degC(1000degF) 200-800 (93-427) .51-.61 

Alloy Ti-95A,Oxidised at 538degC(1000degF) 200-800 (93-427) .35-.48 

Anodized onto SS 200-600 (93-316) .96-.82 

Tungsten 


Unoxidised 212 (100) 0.03 

Unoxidised 932 (500) 0.07 

Unoxidised 1832 (1000) 0.15 

Unoxidised 2732 (1500) 0.23 

Unoxidised 3632 (2000) 0.28 

Filament (Aged) 100 (38) 0.03 

Filament (Aged) 1000 (538) 0.11 

Filament (Aged) 5000 (2760) 0.35 

Uranium Oxide 1880 (1027) 0.79


Zinc 

Bright, Galvanised 100 (38) 0.23 

Commercial 99.1% 500 (260) 0.05 

Galvanised 100 (38) 0.28 

Oxidised 500-1000 (260-538) 

Polished 100 (38) 0.02 

Polished 500 (260) 0.03 

Polished 1000 (538) 0.04 

Polished 2000 (1093) 0.06

Effects of Emissivity 

Thermal imaging cameras detect and measure the sum of infrared energy 

over a range of wavelengths determined by the sensitivity of the camera’s 

detector. Thermal imagers cannot discriminate energy at 7µm from energy at 

14µm the way the human eye can distinguish various wavelengths of light as 

colors. They calculate the temperature objects by detecting and quantifying 

the emitted energy over the operational wavelength range of the detector. 

Temperature is then calculated by relating the measured energy to the 

temperature of a blackbody radiating an equivalent amount of energy 

according to Planck’s Blackbody Law. 

Because the emissivity of an object affects how much energy an object emits, 

emissivity also influences a thermal imager’s temperature calculation. 

Consider the case of two objects at the same temperature, one having high 

emissivity and the other low. Even though the two objects have the same 

temperature, the one with the low emissivity will radiate less energy. 

Consequently, the temperature calculated by the thermal imager will be lower 

than that calculated for the high emissivity object. 


http://www.optotherm.com/emiss-physics.htmc

Discussion 

Subject: They (Infrared Cameras) calculate the temperature of the objects by 

detecting and quantifying the emitted energy over the operational wavelength 

range of the detector. Temperature is then calculated by relating the 

measured energy to the temperature of a blackbody radiating an equivalent 

amount of energy according to Planck’s Black-body Law. 



Apparent Temperature 

Thermal imaging cameras cannot detect the emissivity of objects in order to calculate 

their true temperature. They can only calculate the “apparent” temperature of objects. 

The apparent temperature of an object is a function of both its temperature and 

emissivity. Given two objects with the same true temperature but different emissivity, 

a higher apparent temperature will be calculated for the object with higher emissivity 

(see figure below). Given two objects with the same emissivity but different true 

temperature, a higher apparent temperature will be calculated for the object with 

higher true temperature. The apparent temperature of an object may be substantially 

different from its true temperature. Only when the emissivity of objects is known can 

thermal imagers compensate for emissivity and calculate true temperature. 



Reflectivity 

Objects with high reflectivity can reflect energy radiated by other objects. For example, polished 

aluminum reflects about 90% of the energy incident upon its surface. Just as thermal imagers 

cannot detect the emissivity of objects in order to calculate their true temperature, they also can’t 

detect the reflectivity of objects. Therefore, when calculating the apparent temperature of an 

object, thermal imagers detect and quantify energy emitted from the object, as well as, energy 

reflected from the surface of the object. If an object reflects energy from another radiating source 

with a higher temperature, the apparent temperature that is calculated for the object will be 

higher than its true temperature. Likewise, if an object reflects energy from another radiating 

source with a lower temperature, the apparent temperature that is calculated for the object will be 

lower than its true temperature. 

Given two objects with the same true 

temperature but different emissivity, a higher 

apparent temperature will be calculated for 

the object with higher emissivity (see figure 

below). Given two objects with the same 

emissivity but different true temperature, a 

higher apparent temperature will be 

calculated for the object with higher true 

temperature. The apparent temperature of 

an object may be substantially different from 

its true temperature. Only when the 

emissivity of objects is known can thermal 

imagers compensate for emissivity and 

calculate true temperature. 



Emissivity Makes a Temperature Difference: Blackbody Calibrator 

■ 

https://www.youtube.com/embed/JElKE-ADXr8 


https://www.youtube.com/watch?v=JElKE-ADXr8

What are Emissivity and Reflectivity and How do They Affect Infrared 

Temperature Reading 

■ 

https://www.youtube.com/embed/W5x7fNZTwXE 


https://www.youtube.com/watch?v=W5x7fNZTwXE

Emissivity Examples 

A material’s emissivity can vary at different wavelengths (selective emitter). Most 

materials, however, have relatively uniform emissivity throughout the wavelength 

range in which thermal imagers operate (grey body). For example, the emissivity of 

most plastics, ceramics, and metals does not vary significantly throughout the 

wavelength range of 7 to 14µm. Different materials can have widely different 

emissivity values within the range of 0 to 1.00 (see table below). Many common 

materials including plastics, ceramics, water, and organic materials have high 

emissivity. Uncoated metals may have very low emissivity. Polished stainless steel, 

for example, has an emissivity of approximately 0.1 and therefore emits only one tenth 

the amount of energy of a blackbody at the same temperature. 

Material Emissivity 

Human Skin 0.98 ; Water 0.95 

Aluminum (Polished) 0.10 ; Aluminum (Anodized) 0.65 ; Plastic 0.93 ; Ceramic 0.94 

Glass 0.87 ; Rubber 0.90 ; Cloth 0.95 

Note: The emissivity date in the table above are approximate values. The emissivity 

of a particular material depends on its specific chemical makeup and surface 

characteristics. Smooth, shiny surfaces, for example, tend to have higher reflectivity 

and thus, low emissivity. 



Methods of Increasing Emissivity 

The surface characteristics of a material determine its emissivity. To increase 

a material’s emissivity, it is necessary to increase the emissivity of its surface. 

Following are several methods of altering a material's surface to increase its 

emissivity. For a given material, one or more of these approaches may be 

effective. Choose the method that is easiest to apply/remove and that has 

minimum affect on the temperature of the material. Apply coatings, 

treatments, liquids, tapes, or powders as thin as possible to prevent altering 

the thermal behavior of the original material. Example are: 

• Apply a thin layer of tape 

• Apply a thin layer of paint, lacquer, or other high emissivity coating 

• Apply a thin coating of baby powder or foot powder 

• Apply a thin layer of oil, water, or other high emissivity liquid 

• Apply a surface treatment such as anodizing 

• Roughen the surface (may require substantial roughening) 



End Of Reading 


Good Luck 


Good Luck 


Charlie https://www.yumpu.com/en/browse/user/charliechong 

Chong/ Fion Zhang

Understanding Infrared Thermal Testing Reading 2

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