FLIR R&D Handbook - Infrared Camera Warehouse

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The Ultimate Infrared Handbook
for R&D Professionals
The Ultimate Resource Guide for
Using Infrared in the Research and
Development Industry
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ii
Contents
IR Thermography – How It Works
1
IR Detectors For Thermographic Imaging
7
Getting The Most From Your IR Camera
15
Filters Extend IR Camera Usefulness
26
Ultrahigh-Speed Thermography
36
iii
iv
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
(0.9–14 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 noncontact measurements of an object’s
temperature.
IR camera construction is similar to
a digital video 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
IR In
NIR
MWIR
LWIR
Detector Cooling
Digitization
Optics
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. Still, microbolometers react
to incident radiant energy and are much
slower 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
Video
Processing
Electronics
User Interface
User Control
Video Output
Digital Output
Synchronization In/Out
System Status
Figure 1. Simplified block diagram of an IR camera
1
Chapter 1
cooling, sometimes down to cryogenic
temperatures using liquid nitrogen or a
small Stirling cycle refrigerator unit.
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 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.,
MCT
PtSi
QWIP
InSb
Microbolometer
MWIR
3.0µm
LWIR
5.0µm
8.0µm
14.0µm
Figure 2. Examples of detector materials and
their spectral responses relative to IR midwave
(MW) and longwave (LW) bands
2
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.
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 = aW + rW + tW,
which can be simplified to:
1 = a + r + t.
The coefficients a, r, and t describe the
object’s incident energy absorbtion
(a), reflection (r), and transmission (t).
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 r = 0,
t = 0, and a = 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
IR Thermography – How It Works
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.
The total radiation law can thus take the
mathematical form 1 = e + r + t, which for
an opaque body (t = 0) can be simplified
to 1 = e + r or r = 1 – e (i.e., reflection =
1 – emissivity). Since a perfect blackbody
is a perfect absorber, r = 0 and e = 1.
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 e, the emittance
or emissivity of the body. Kirchhoff’s law
states that a = e, and since both values
vary with the radiation wavelength, the
formula can take the form a(l) = e(l),
where l denotes the wavelength.
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 3).
These curves show the radiation per
wavelength unit and area unit, called
the spectral radiant emittance of the
blackbody. The higher the temperature,
4.50
T-1000˚C
Blackbody spectral radiant emittance
4.00
3.50
3.00
T-900˚C
2.50
2.00
T-700˚C
T-800˚C
T-600˚C
1.50
T-500˚C
T-400˚C
1.00
T-300˚C
0.50
T-200˚C
0.00
Visible
light
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Figure 3. Illustration of Planck’s Law
3
Chapter 1
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,
lmax = 2898/T,
where T is the absolute temperature
of the blackbody, measured in Kelvin
(K), and lmax is the wavelength at the
maximum intensity. 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.
From Planck’s law, the total radiated
energy from a blackbody can be
calculated. This is expressed by a formula
known as the Stefan-Bolzmann law,
W = σT4 (W/m2),
where σ is the Stefan-Bolzmann’s
constant (5.67 × 10–8 W/m2K4). As an
example, a human being with a normal
temperature (about 300 K) will radiate
about 500W/m2 of effective body
surface. As a rule of thumb, the effective
body surface is 1m2, 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.
4
Emissivity
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 Wbb, and that of a normal
object at the same temperature as Wobj,
then the ratio between these two values
describes the emissivity (e) of the object,
e = Wobj / Wbb.
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 e
for all wavelengths is called a greybody.
Consequently, for a greybody, StefanBolzmann’s law takes the form
W = eσT4 (W/m2),
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 e
for the object.
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.
IR Thermography – How It Works
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.
For thermographic measurement we
must use the so-called atmospheric
windows. As can be seen from Figure 4,
they can be found between 2 and 5µm,
the mid-wave windows, and 7.5–13.5µm,
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 7.5–13.5μm
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.
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
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 highest IR transmission.
5
Chapter 1
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).
The total radiation power received by the
camera can now be written:
Given this situation, we can derive a
formula for the calculation of the object’s
temperature from a calibrated camera’s
output.
where e is the object emissivity, t is the
transmission through the atmosphere,
Tamb is the (effective) temperature of the
object’s surroundings, or the reflected
ambient (background) temperature,
and Tatm is the temperature of the
atmosphere.
1. Emission from the object = e · t · Wobj,
where e is the emissivity of the object
and t is the transmittance of the
atmosphere.
2. Reflected emission from ambient sources
= (1 – e) · t · Wamb, where (1 – e) is the
reflectance of the object. (It is assumed
that the temperature Tamb 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 – t) · Watm, where (1 – t) is the
emissivity of the atmosphere.
6
Wtot = (1 – t) · Wobj + (1 – e) · t · Wamb +
(1 – t) · Watm,
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.
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
1). Most detectors have a response curve
that is narrower than the full IR range
(900–14,000 nanometers or 0.9–14µm).
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
IR In
NIR
MWIR
LWIR
Detector Cooling
Digitization
Optics
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 2–5.6µm range, the
short/medium wavelength (SW/MW) IR
band, and one in the 8–14µm range, the
long-wavelength (LW) IR band. There are
many detector materials and cameras
with response curves that meet these
criteria.
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
~12ms)
Video
Processing
Electronics
User Interface
User Control
Video Output
Digital Output
Synchronization In/Out
System Status
Figure 1. Simplified block diagram of an IR camera
7
Chapter 2
1012
D · (cm√Hz/W)
LP
H
OT OT
OC OVO
LTAIC
ON
DU
CTO
R
K
95
s2
Pb
K
193
Se
Pb
77K
Se
b
P
5K
29
Se
Pb
G
:G
Si
6K
g2
e:H
K
.2
s4
K
4.2
Sb
Si:
10 9
PH
OT
ID OVO
EA
L
L P TAIC
HO
TOCONDUCTOR
1011
K
.2
a4
:A
Si
2π STERADIANS FIELD OF VIEW
295K BACKGROUND TEMPERATURE
L
EA
ID
1010
7K
s7
Pb
EA
ID PH
L
EA
ID
1011
D · (cm√Hz/W)
s
Pb
1012
2π STERADIANS FIELD OF VIEW
295K BACKGROUND TEMPERATURE
3K
19
K
77
V)
7K
)7
e (P
3K
PV
dT
(
C
) 19
b
Hg
PC
(
InS
Te
d
C
Hg
1010
K
77
V)
(P
K
Te
d
77
C
C)
Hg
(P
Te
d
C
Hg
10 9
Bolometer (90Hz)
QWIP
Pyroelectric Det. (90Hz)
10 8
1.0
1.5 2.0 2.5 3
4
5
6 7 8 9 10
15
20 25 30
40
10 8
1.0
1.5 2.0 2.5 3
Wavelength (µm)
4
5
6 7 8 9 10
15
20 25 30
40
Wavelength (µm)
Figure 2. Detectivity (D*) curves for different detector materials
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). Table 1 lists some
of the most commonly used detectors in
today’s IR cameras.
8
Table 1. Detector types and materials commonly
used in IR cameras.
Detector Type/
Material
Microbolometer
HgCdTe
HgCdTe
InSb
PtSi
QWIP
Operation
Broadband
detector
SW photon
detector
LW photon
detector
MW photon
detector
MW photon
detector
LW photon
detector
Operating
Temp.
Uncooled
(~30°C)
200 K
77 K
77 K
77 K
70 K
IR Detectors For Thermographic Imaging
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 Figure 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 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 3). 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,
Figure 3. Operating principle of quantum detectors
9
Chapter 2
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.
Cold side
Copper
Thermoelectrical
material
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
4 and 5.
Warm side
+
–
DC
Figure 4. Single stage Peltier cooler
Mounting plate
IR detector
Figure 5. Three-stage Peltier cooler
10
Figure 6. Integrated Stirling cooler, working
with helium gas, cooling down to –196ºC or
sometimes even lower temperatures
IR Detectors For Thermographic Imaging
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 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.
Focal Plane Array Assemblies
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
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.
Figure 7. Examples of cooled focal plane array
assemblies used in IR cameras
Figure 8. QWIP FPA mounted on a ceramics
substrate and bonded to external electronics
11
Chapter 2
Signal
Without any correction
Signal
First correction step
Radiation
–20°C
Radiation
+120°C
–20°C
+20°C
+120°C
+20°C
Figure 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.
Signal
First correction step
Signal
Second correction
A/D Dynamics
Radiation
–20°C
Radiation
+120°C
–20°C
+20°C
+120°C
+20°C
Figure 10. After offset compensation, slope correction is applied.
Signal
Third correction,
Non-Uniformity Correction (NUC)
Signal
After NUC
Radiation
–20°C
+120°C
+20°C
Radiation
–20°C
+120°C
+20°C
Figure 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.
12
IR Detectors For Thermographic Imaging
performed by the camera software. See
Figures 9–11.
The ultimate result is a thermographic
image that accurately portrays relative
temperatures across the target object
or scene (Figure 12). Moreover, actual
temperatures can be calculated to within
approximately ±1°C accuracy.
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
Figure 12. IR image from a 1024 × 1024 InSb
detector camera
Relative
sensitivity
100%
InSb
90%
MCT-SW
Mikrobolometer
80%
InGaAs
MCT-LW
FLIR QWIP
VisGaAs
70%
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
8
9
10
Wavelength λ [µm]
11
12
13
14
15
16
17
18
Figure 13. Relative response curves for a number of IR cameras
13
Chapter 2
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.
14
Once a detector is selected, lens material
and filters can be selected to somewhat
alter the overall response characteristics
of an IR camera system. Figure 13 shows
the system response for a number of
different detectors.
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 thermographic and
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 (0.9–14 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:
• Given a linear sensor, measured
radiance is linear with incident energy.
15
Chapter 3
There are five basic steps in producing
radiometric and thermographic
measurements with an IR camera system:
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 ThermoVision® 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.
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.
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.
•
•
•
•
Temperature is non-linear with raw
digital image counts, even with a linear
sensor.
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.
The relationship between spectral
bands of interest can be much easier
to determine if you are working within
radiometric units.
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 bandpass effects.)
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
16
Getting The Most From Your IR Camera
I (T) = ∫ R (λ) · LBB(λ, T)
1,1
1
LBB (λ, T)
R(λ)
0,9
0,8
0,7
0,6
0,5
0,4
I (T)
0,3
0,2
0,1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Figure 1. The surface under the curve I(T) describes the system response signal for a blackbody
radiator at a particular temperature.
This produces a radiometric calibration of
in-band radiance [W/(sr-cm2)] 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 1.
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
ThermoVision® product line), have internal
sensors that monitor changes in camera
17
Chapter 3
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
its emission, reflected emission from
ambient sources, and emission from the
atmosphere using the Total Radiation
Law. The total radiation power received
by the camera can be expressed as:
Wtot = e · t · Wobj + (1 – e) · t · Wamb +
(1 – t) · Watm,
where e is the object emissivity, t is the
transmission through the atmosphere,
Tamb is the (effective) temperature of the
object surroundings, or the reflected
ambient (background) temperature,
and Tatm 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.
18
Calibration and analysis software
tools available to users are not always
contained onboard the camera. While
high-end cameras have many builtin 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 ThermaCAM® 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:
Getting The Most From Your IR Camera
• 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
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 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).
Figure 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 4
illustrates two gray scale possibilities.
In Figure 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
19
Chapter 3
Figure 4. Gray scale images of car engine; left view has white as the hottest temperature; right view
shows black as the hottest
rendered as either lighter colors or darker
colors. Another possibility is rendering
images with what is known as a rainbow
scale (Figure 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).
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.
Figure 5. Rainbow scale showing lower
temperatures towards the blue end of the
spectrum
20
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 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 unless all of the
Figure 6. Isotherm color scale with each color
having an isotherm width of 2°C
Getting The Most From Your IR Camera
highlighted area has the same emissivity,
and 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.
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 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
21
Chapter 3
Figure 7a. PCB heated to a uniform 68.7°C, but
digital readouts are incorrect.
Figure 7b. PCB with emissivity correction using
the “adjacent spot” technique. Digital readouts
now indicate the correct temperatures at all
locations.
calculated. The corrected temperatures
are shown in Figure 7b.
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.
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.
22
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 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.
Getting The Most From Your IR Camera
To use this information consider, the pixel
IFOV relative to the target object size
(Figure 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.
Figure 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 9 illustrates this concept.
Figure 9. A camera’s geometric (spatial)
resolution (IFOV) is determined by its lens and
FPA configuration.
Figure 10. IFOV (red squares) relative to object
size.
In the right view of Figure 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.
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
23
Chapter 3
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
__ – SSR
____ , where
S 1
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
24
radiance, radiant intensity, temperature,
target length/area, etc. Optional software
modules are also available for spatial
and spectral radiometric calibration.
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.
Getting The Most From Your IR Camera
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.
25
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.
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 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
Relative
sensitivity
100%
InSb
90%
MCT-SW
Mikrobolometer
MCT-LW
FLIR QWIP
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
8
9
10
Wavelength λ [µm]
11
Figure 1. Relative response curves for a number of IR cameras
26
12
13
14
15
16
17
18
Filters Extend IR Camera Usefulness
opens the possibility of measuring the
radiance and temperature of a selective
radiator at some wavelength.
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 2.
SP and LP filters are specified with a
27
Chapter 4
Different types of filter characteristics
100
90
80
70
System response curve
60
Long-pass filter
50
Band-pass filter
40
Narrow band-pass filter
30
Short-pass filter
20
10
0
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Wavelength, µm
Figure 2. Response curves for different types of filters
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.
An example of applying a NBP filter
to the measurement of polyethylene
film temperature is shown in Figure 3.
The blue curve in the figure shows the
absorption band of polyethylene film.
The red curve shows the transmittance
28
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.
Filters Extend IR Camera Usefulness
Filter adaptation
1
0.9
Transmission %
0.8
3.45µm NBP filter
0.7
0.6
Polyethylene
transmission
0.5
0.4
Resulting
transmission
0.3
0.2
0.1
0
3
3.1
3.2 3.3 3.4
3.5 3.6 3.7 3.8
3.9
4
Wavelength, µm
Figure 3. Application of an NBP filter to achieve nearly complete absorption and high emittance
from polyethylene film, allowing its temperature measurement
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.
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 non-contact
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 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,
rl + el + tl = 1, or el = 1 – tl – rl ,
it is clear that in order to get an emissivity
value, transmittance and reflectance
at the pass band of the filter must be
29
Chapter 4
Spectral transmission of polyethylene
100
90
80
70
60
50
40
25µm
125µm
250µm
30
20
10
0
2
3
4
5
6
7
8
9
10
11
12
13
14
Wavelength, µm
Figure 4. Transmission bands for polyethylene films of three different thicknesses
known. The transmittance, t l , can
be taken directly from a transmission
diagram like the one in Figure 3 (a value
of about 0.02 in that example).
Reflectance is less easy to characterize
and usually is a function of material
thickness. However, a transmission
diagram like the one in Figure 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
30
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. At
the absorption band, however, since
the absorption in the material is almost
complete, we get reflection only on one
side. Thus rl = 0.04.
From this rl , and the t l value obtained
from the transmission graph (Figure
3 in this example), emissivity can be
calculated:
el = 1 – 0.02 – 0.04 = 0.94.
This value is entered into the camera’s
measurement database before having it
Filters Extend IR Camera Usefulness
Spectral transmittance of Soda-Lime-Silica glass. Glass thickness in mm.
100
0 .23
Transmittance %
80
0.7
60
1 .6
40
3. 2
20
5. 9
0
2 .5
3
3 .5
4
4 .5
5
5 .5
6
6 .5
7
7 .5
8
Wavelength, µm
Figure 5. Transmission curves for a common industrial glass in five thicknesses from 0.23 to 5.9mm
calculate the temperatures from radiance
observations.
Sheet and plate glass production have
similar temperature measurement
requirements. The most common
industrial varieties are variations of sodalime-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 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 midwavelength (MW) or
long wavelength (LW) camera/detector.
MW cameras cover some portion of the
spectrum from 2–5μm, and LW cameras
cover some portion within 8–12μm.
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 thermoelectrically cooled HgCdTe (MCT)
detectors, which are used in both MW
and LW cameras. A better choice may be
a NBP filter.
In Figure 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.
The reflectance of this glass is shown in
Figure 7. Note the peak between 8 and
12µm, which must be avoided when using
an LW camera to measure the glass.
31
Chapter 4
Spectral adaptation to glass
100
90
Glass transmission curve
Transmission %
80
70
SW/TE MCT spectral response
60
50
4.7µm LP filter curve
40
30
5.0µm NBP filter curve
20
10
0
1. 5
2
2. 5
3
3. 5
4
4. 5
5
5. 5
6
Wavelength, µm
Figure 6. Two alternative filters for glass measurement with a SW camera
Spectral reflectance of Soda-Lime-Silica glass at normal incidence
50
40
30
20
10
0
2
4
6
8
10
12
14
16
Wavelength, µm
Figure 7. Reflectance of a common glass at normal (perpendicular) incidence
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 8).
32
From Figure 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
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.
Filters Extend IR Camera Usefulness
Reflectance of Soda-Lime-Silica glass for
a varying angle of incidence at 5µm
0 .1 4
0 .1 2
0 .1 0
0 .0 8
0 .0 6
0 .0 4
0 .0 2
0 .0 0
0
10
20
30
40
50
60
70
Angle of incidence, degrees
Figure 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 H2O+CO2 around 3µm or CO2 at 4.2µm.
Atmospheric effects on an LW camera
are much less, since the atmosphere has
an excellent window from 8 to 12µm.
However, cameras with a broad response
curve reaching into the MW spectrum
may require an LP filter. This is particularly
33
Chapter 4
Relative
Intensity
3.9µm flame filter
4.3µm CO2 filter
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
1.5
2
2.5
3
3.5
4
4.5
5
Wavelength λ [µm]
Figure 9. Flame absorption spectrum of a gas-fired furnace with two types for filters for different
measurement applications
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 7.4µm 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
9 reveals the spectral regions where these
34
two types of measurement could be
made. There is very little radiation from
the flames in the 3.9µm 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.
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 CO2 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 and 4.02µm. With this filter installed,
the camera will produce an image where
the flames are almost invisible and
Ultrahigh-Speed Thermography
the internal structure of the furnace is
presented clearly (Figure 10).
To get the maximum temperature of
the flames, a CO2 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.
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 wellcalibrated 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.
Figure 10. FLIR ThermaCAM® image of furnace
tubes with flame filter to allow accurate
temperature measurement
35
Chapter 5
Ultrahigh-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 highspeed 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 highspeed 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, highspeed 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 highspeed 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
36
Ultrahigh-Speed Thermography
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
Figure 1a. Infrared image of a 0.30 caliber bullet
in flight with apparent temperatures
axis of fire to within a distance of several
centimeters.
Figure 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 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.
High-Speed Imaging for Fast Transients
Short integration times and highspeed frame rates are not always
paired together in IR cameras. Many
Figure 1b. Visible-light image of an identical
0.30 caliber bullet in flight
37
Chapter 5
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 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
high-speed 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.
Figure 2. Integrated circuit with 800ms
overcurrent pulse
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 3).
38
Integrated Circuit Example
100
80
60
40
20
0
1
2
3
4
5
6
7
8
9
10
11
Time (ms)
Actual Data
Under Sampled Data
Figure 3. Maximum IC temperature data – actual
vs. undersampled
This is just one example of why highspeed IR cameras can be so valuable
for even simple applications that don’t
necessarily appear to benefit from high
speed at first consideration.
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.
High-speed 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 4 illustrates
Ultrahigh-Speed Thermography
the increase in frame rates relative to user
defined window sizes.
Figure 4. Example of FPA window sizes relative
to frame rates
Newer camera designs offer 16 channels
and pixel clocks upwards of 205
megapixels/second. This allows for very
fast frame rates without sacrificing the
window size and overall resolution.
Preset Sequencing Increases
Dynamic Range
High-speed IR cameras have an
additional benefit that does not relate
to high-speed 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 18–22 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, captured at two different
Figure 5. Active aircraft engine imaged at integration rates of 2ms (left) and 30µs (right)
39
Chapter 5
integration times. 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 (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 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 6).
Figure 6. Superframed image created with
ABATER software from Preset 0 and Preset 1
data.
As you may have figured out, the down
side to this method of data collection
and analysis is the reduction in the frame
40
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.
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.
Ultrahigh-Speed Thermography
41
TRY Before
You BUY!
Renting is perfect for short-term applications, pre-purchase evaluation, or to temporarily replace a thermal imaging
camera in for service. The FLIR Infrared Camera Rental Program offers the largest inventory of the latest thermal
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The more thermal imaging cameras you rent, the more you save! Renting an infrared camera from
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Our Rental Inventory Includes:
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ThermaCAM® S65
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42
Photon
A320
A320G
A20
A40
S65
SC640
μbolometer
μbolometer
μbolometer
μbolometer
μbolometer
μbolometer
μbolometer
Pixel Resolution
Pixel Pitch
324×256
38μm
320×240
25μm
320×240
25μm
160×120
35μm
320×240
38μm
320×240
38μm
640×480
25μm
Spectral Ranges
7.5μm – 13.5μm
7.5μm – 13.0μm
7.5μm – 13.0μm
7.5μm – 13.5μm
7.5μm – 13.5μm
7.5μm – 13.5μm
7.5μm – 13.5μm
14-bit
•
16-bit
•
•
16-bit
•
•
14-bit
•
•
14-bit
•
•
14-bit
•
•
14-bit
•
•
30 fps
30 fps
60 fps
60 fps
30 fps
30 fps
30 fps
Digital Data Output
GigE or Serial
Ethernet
GigE
1394 Firewire
1394 Firewire
1394 Firewire
1394 Firewire
Analog Video
Command and Control
RS-170
RS-232 or GigE
RS-170
Ethernet
GigE
RS-170
1394 Firewire, RS-232
RS-170
1394 Firewire, RS-232
RS-170
1394 Firewire, RS-232
RS-170, S-Video
USB, 1394 Firewire
•
•
•
•
Optional
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
f/1.3
f/1.3
f/1.2
f/1.0
f/1.0
f/1.1
Sensor Type
Dynamic Range
Internal Temp Calibration
Ambient Drift Compensation
Pixel Clock
Full Frame Rate
Windowing
Motorized Focus
Auto Focus
Built-in IRIG-B timing
Triggering Options
Internal Battery Operation
Viewfinder/LCD Display
On Board Image Storage
SDK Support
Lab View Compatibility
Aperture
•
•
•
f/1.3, f/1.4, f/1.4, f/1.7
lens dependent
Filtering Options
Available Optics
•
The Photon is
available with the
following fixed lens
configurations:
14.25mm, 19mm,
35mm, 50mm.
18mm
30mm
10mm
18mm
30mm
10mm
36mm
17mm
9.2mm
124mm
72mm
36mm
18mm
9mm
Close-up 150µm
Close-up 80µm
Close-up 50µm
124mm
72mm
36mm
18mm
9mm
Close-up 150µm
Close-up 80µm
Close-up 50µm
76mm
40mm
19mm
Close up 50µm
SC4000
InGaAs, VisGaAs,
InSb, QWIP
320×256
30μm
0.4μm – 1.7μm
0.9μm – 1.7μm
1.5μm – 5.2μm
3.0μm – 5.0μm
8.0μm – 9.2μm
14-bit
• (InSb, QWIP)
•
50MHz
433 fps
Simultaneous
GigE and Camera Link
RS-170
USB, GigE, RS-232
Random Size and
Location
Optional
Optional
•
•
•
•
f/2.5, f/4.1,
(InGaAs-Variable)
•
InSb
1 meter
350mm
200mm
100mm
50mm
25mm
13mm
1× Microscope
2.5× Microscope
4× Microscope
5× Microscope
60/180/500mm
50/250mm
SC6000
InGaAs, VisGaAs,
InSb, QWIP
640×512
25μm
0.4μm – 1.7μm
0.9μm – 1.7μm
1.5μm – 5.2μm
3.0μm – 5.0μm
8.0μm – 9.2μm
14-bit
• (InSb, QWIP)
•
50MHz
132 fps
Simultaneous
GigE and Camera Link
RS-170
USB, GigE, RS-232
Random Size and
Location
Optional
Optional
•
•
•
•
f/2.5, f/4.1,
(InGaAs-Variable)
•
QWIP
100mm
50mm 25mm
13mm
60/180/500mm
InGaAs
100mm (640×512)
75mm (640×512)
50mm
25mm
16mm (320×256)
8mm (320×256)
STRATUS
SC8000
InGaAs
InSb
320×256
30μm
1024×1024
18μm
0.9μm – 1.7μm
3.0μm – 5.0μm
14-bit
14-bit
205MHz
2,300 fps
205MHz
132 fps
Camera Link Full
GigE, Camera Link Full
RS-232 or 422
Random Size and
Location
USB, GigE
Random Size and
Location
•
•
•
•
•
•
•
Variable
f/4.0
•
50mm
25mm
16mm
8mm
1 meter
100mm
50mm
25mm
Published by FLIR Systems Incorporated
www.infraredresearchcameras.com • 1 800 464 6372
Version 1
T0001PL