Infrared Physics & Technology 87 (2017) 47–54

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Infrared Physics & Technology
journal homepage: www.elsevier.com/locate/infrared

Regular article

Passive imaging of wind surface flow using an infrared camera
Nathan Hagen
Utsunomiya University, Dept. of Optical Engineering, Center for Optical Research and Engineering (CORE), Utsunomiya, Japanh i g h l i g h t s
 Second-derivative temporal filtering on a thermal imaging video stream.
 Millidegree thermal dynamics show effect of turbulent air flow crossing a surface.
 Passive visualization of wind surface flow.
 Easy to implement for real-time view of flow dynamics.

a r t i c l e

i n f oArticle history:
Received 15 December 2016
Revised 29 September 2017
Accepted 30 September 2017
Available online 3 October 2017
Keywords:
Infrared imaging
Passive remote sensing
Atmospheric optics
Turbulence
Motion estimation
Optical flowa b s t r a c t
We present a method for passive imaging of wind motion against surfaces in a scene using an infrared
video camera. Because the method does not require the introduction of contrast agents for visualization,
it is possible to obtain real-time surface flow measurements across large areas and in natural outdoor
conditions, without prior preparation of surfaces. We show that this method can be used not just for
obtaining single snapshot images but also for real-time flow video, and demonstrate that it is possible
to measure under a wide range of conditions.
Ó 2017 Elsevier B.V. All rights reserved.

Wind plays a large role in measurements taken outdoors, andyet its speed and direction can be quite difficult to measure. Visualizing the complex swirls and eddies created by turbulent wind
patterns near the ground, and especially around objects, requires
the use of a real-time imaging technique that is compact and capable of a wide field of view.
There are a number of experimental techniques commonly used
for flow visualization [1]. While these techniques have been shown
to work well, they generally require the introduction of contrast
agents into the flow, or the use of controlled light sources, so that
operating outdoors or analyzing the flow around large objects
becomes difficult. Imaging smoke trails, for example, requires having a smoke generator and a structure for controlled releases ofsmoke streams. Schlieren imaging and shadowgraphy both requirea spatially coherent light source in line with the imaging camera,
and require a refractive index contrast within the flow. Planar
laser-induced fluorescence imaging and Rayleigh scattering imaging are also popular methods, but require high-power pulsed lightsources. And as in the case of imaging smoke trails, these methods
E-mail address: hagen@cc.utsunomiya-u.ac.jp
https://doi.org/10.1016/j.infrared.2017.09.023
1350-4495/Ó 2017 Elsevier B.V. All rights reserved.

require the introduction of a contrast agent – scattering particles ora fluorescent gas – in order to measure flow.
A passive method for measuring wind surface flow that requires
only a single infrared video camera and simple video processing to
implement thus provides access to measurements that are otherwise difficult to perform. This technique differs from most existingflow visualization methods in that it measures surface flow rather
than bulk volumetric flow, a feature that can be an advantage ora detriment, depending on the application [2,3]. Inagaki et al. havepreviously demonstrated [4] the feasibility of infrared imaging ofwind surface flow under favorable viewing conditions of strong
thermal contrast between the surface and the ambient air, and of
relatively smooth flow that allows for time-averaging to compose
an image. We demonstrate in a series of experiments that not onlyis it possible to image in unfavorable conditions such as during
light rain, but that it is also possible to obtain video measurements
so that flow estimation can be performed in real time (up to 500
frames/s), for fast and highly turbulent flows.
The technique shown here of analyzing thermal dynamics ofobjects is closely related to thermal wave imaging [5] andpulse thermography [6], with the difference that, instead of being

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54

delivered by a controlled source, the ‘‘pulses” are uncontrolled andare delivered by the wind without active intervention by theviewer. By analyzing the temporal changes in the surface temperature rather than the temperature itself [7], wind turbulence
becomes the contrast-inducing agent for visualization.
1. Analysis of surface thermal interaction with turbulent wind
Fig. 1 shows an example measurement processed for flow visualization, in which an infrared camera is looking out at a scene
composed primarily of solar-illuminated concrete on a warm afternoon, with 35 °C air temperature, 75% relative humidity, and concrete at 50 °C surface temperature. The camera used for thismeasurement is an InSb detector array (IRCameras LLC, QuazIRSD) with a 640  512 image size, 20 lm pixels, and 14-bit output
at 60 frames/s. The detector is cryo-cooled to 77 K and operates
across the MWIR spectral range with a cold filter transmitting from2.95 to 4.97 lm. The camera uses an f =2:3 lens (Janos Technology
Inc., ASIO 50 mm) with a 50 mm focal length. With this configuration, the camera NETD is 15 mK (at 23 °C), and the integration timeused was 2 ms. Faster readout rates can be achieved by cropping
the image, up to the point where the integration time limits theframe rate to 500 frames/s.

Fig. 1. A temporally-filtered image of a scene composed primarily of solarilluminated concrete (see Video 1 in supplementary data). A colleague is adjusting
equipment at the right side of the image, providing some context for the speed of
the wind flow and the scene field of view. Red arrows have been added to indicate
the local direction of wind flow along the concrete. The occasional dark and brightcircles crossing the field of view in the video are due to insects flying across the fieldof view, seen as blurred circles because they are close to the camera and out offocus. In this image, as in Figs. 4 and 6 below, the filtered image grayscale display
has been linearly scaled to black/white at brightness temperature differences of75 mK. (For interpretation of the references to color in this figure legend, thereader is referred to the web version of this article.)

Lobj ðkÞ ¼ obj ðkÞ Bðk; T obj Þ þ qobj ðkÞ Lbkg ðkÞ:Video 1The data shown in Fig. 1 shows that when turbulent cool air
passes across a warm surface, some surface regions are cooled
more than others, due to differences in surface geometry, small
temperature differences in the air passing over the surface, andto changes in the way that the air interacts with the surface. These
small temperature differences decay over time as the heat conducts into the material beneath the surface, or a parcel of air withdifferent temperature passes over the surface.
The striations visible in Fig. 1 are generally  3 pixels apartfrom one another along their cross-stream (spanwise) dimension,
which at this imaging distance of 8 m corresponds to a width of
12 mm. Because the concrete surface is observed at an obliqueangle of h  75 , the streaks appear closer together than theyphysically occur at the concrete surface, by a factor ofcos h  0:26, so that the actual physical spacing across the concrete is about 46 mm.
In order to understand the physical phenomenon producing thestriations we see in Fig. 1, we model the change in radiance viewed
by the camera due to changes in temperature of an object in thescene and provide a simple model to analyze the dynamic thermal
behavior of the imaged surface. The brightness of a surface in thethermal infrared is governed by the Planck blackbody law, mediated by the object’s spectral emissivity and reflection of background light:

where Lobj ðkÞ is the object surface spectral radiance, B is the Planck
blackbody spectral radiance for an object with surface temperature
T;  and q are the emissivity and reflectivity of the surface (one canoften assume q ¼ 1  ), and Lbkg is the background spectral radiance reflected from the surface. We ignore the small absorption
and emission effects of the atmosphere between the imaged surface
and the camera. Integrating Lobj ðkÞ over the wavelength range ofinterest gives the total radiance, so that for a fixed background radiance Lbkg ðkÞ and surface emissivity , the total radiance can be used
to measure the surface temperature T. For all of the temperature
data we present below, we will normalize effective temperatures
to a reference emissivity of 0.95 – the measured emissivity of ourblackbody, while also close to typical values for both concrete andunvarnished wood in the longwave infrared spectral region [8].
A surface exposed to a thermal gradient undergoes a change intemperature governed by the heat conduction equation, giving thesurface temperature TðrÞ as a function of the position r ¼ ðx; y; zÞand time t [9]r2 Tðr; tÞ ¼

1 @Tðr; tÞ
;
@t

a

ð1Þ

for diffusivity coefficient a ¼ k=ðqcp Þ, heat conductivity k (W m1
K1), mass density q (kg m3), and specific heat capacity cp (Jkg1 K1). Representative values for the thermal properties of several materials are given in Table 1 for convenience, as these materials appear in the wind flow videos.
To simplify the complex thermal interaction between the fluid(the wind), the solid surface, and the boundary layer separating
the two, we can consider rapid changes in the fluid motion as
inducing impulse changes in the surface temperature. If weapproximate the effect of the wind to cool or heat a surface overa short period as inducing a 2D Gaussian temperature profile
across the surface, the temperature response of a semi-infinite
solid object is given in a cylindrical coordinates solution as [10]49

N. Hagen / Infrared Physics & Technology 87 (2017) 47–54
Table 1
Material thermal and optical properties: mass density q, thermal conductivity k, specific heat capacity cp , and typical emissivity .

q (kg m3)

Materialk (W m1 K1)cp (J kg1 K1)

a (m2 s1)



5

0.20

Aluminum2700

204–250900

9:3  10Concrete

2000

0.8–2.5880

9:4  107

0.94

Wood (pine)373

0.09–0.21

1200–2900

2:0  107

0.92

Fig. 2. The radial surface temperature profile as a function of time, calculated from
Eq. (2).



z2
r2

exp

;

4at 4at þ d2
qcp ðpatÞ1=2 ð4at þ d2 Þ2Tðr; z; tÞ ¼

dðtÞ E0 d

ð2Þ

for r the radial distance from the pulse center, z the depth coordinate, d the Gaussian profile diameter, dðtÞ the temporal impulse
function, and E0 the energy transferred to/from the surface by thepulse. Fig. 2 shows the calculated temperature response of a concrete surface to a pulse as a function of time after the pulse is delivered. From Fig. 2, we can see that, for concrete, an instantaneouschange in surface temperature of 20 mK will relax below the visibility limit of the camera (<5 mK) after a period of about 5 s.
So far we have discussed the fluid-surface interaction in
abstract terms, but many researchers have observed large-scalecoherent flow structures in turbulent boundary layers, and streamwise streaks in particular [11–14]. In the near-surface region of theflow, the predominant coherent structures are low- and highspeed streaks and streamwise vortices, each of which have a characteristic spanwise spacing of Dx, observed to be about 100 wall
units: Dx  100m=us , where m is the kinematic viscosity and us
the friction velocity [12]. In the case of the observations shownin Figs. 1, 4, 6, and 9, we can estimate u = 0.15 m s1 [15] andm ¼ 1:69  10 5 m2 s1 , giving a rough estimate of Dx  11 mm
for the spacing between striations. The disagreement between thisspacing and the spacing observed above (46 mm) likely indicatesthat the data is not imaging individual striations but rather is averaging over a collection of them. (See the discussion on spatial sampling in Section 3 below for more detail.)
The thermal striations we observe in Fig. 1 are likely producedby rapid exchanges of (warm) low-velocity air lifted away from thesurface with (cool) high-velocity air pushed in towards the surface[16]. These exchanges are cross-stream (spanwise) motions of air
induced by the streamwise vortices, causing the rapid changes insurface temperature indicated by the thermal camera. Conduction
with the underlying material substrate causes these thermal fluctuations to relax over a period of seconds, until the thermal change
falls below the measurement noise.

to the several millikelvin level, at which level of detail we begin
to see thermal behavior that is not otherwise apparent. For analyzing these dynamics, a wide variety of temporal filters are possible,
but all of them implement a form of second-order derivative filter[17], with a high-frequency cutoff for noise suppression and a lowfrequency cutoff that defines the minimum rate of the dynamics.
While it is possible to visualize flow using a simple temporal gradient filter, the raw measurement noise in this case makes it difficult to see all but the largest amplitude striations. Thus, taking atemporal average prior to calculating the temporal gradient
reduces the noise significantly and generally does not reduce thethermal signal, which can require a second or more to reach its fullvalue. If the average is taken over too long a period, however, thenslow global variations in temperature tend to dominate over the
faster local thermal dynamics induced by wind turbulence. Thus,
the second-order derivative filter is designed to pass the middle
frequency range while removing the global change and suppressing noise.
Common second-order filters include the Laplacian of a Gaussian and the discrete Laplacian filter. For temporal filtering, the latter is easily constructed as the difference between two temporalmoving averages (see Fig. 3) [17]. This form is particularly appropriate for high-speed processing due to its simplicity, and was
the algorithm implemented to produce the video of Fig. 1:

2. Temporal filtering of a thermal video sequence
Temporal filtering of the infrared video sequence allows us toanalyze the temperature changes dT of objects in a scene down

Fig. 3. (a) The temporal filter used for the videos visualizing wind. (b) In thefrequency domain, the filter (a) takes the form of a Shannon mother wavelet [18].

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54

Fig. 4. The temporally-filtered thermal image of a scene consisting primarily of a vertical wood board (unlaminated masonite) exposed to oblique illumination by the Sun on
a partially cloudy afternoon (see Video 2 in supplementary data). The board and equipment are shown in outlines drawn on the image.

dTðx; yÞ ¼ ðavg of N 1 framesÞ  ðavg of N2 framesÞ
¼ T local ðx; yÞ  T trend ðx; yÞ:

ð3Þ
ð4Þ

For all of the visualization videos we discuss, the filter parameters used were N 1 ¼ 4 frames;N 2 ¼ 80 frames, corresponding toaveraging times of 67 ms and 1.3 s respectively. The longbaseline moving average T trend removes the overall cooling trend
in the data, while the short-baseline moving average T local gives
the current temperature, averaged over a few frames in order toreduce noise.
In general, the videos shown here are not overly sensitive to theparameter values, so that there is a good deal of flexibility in the
choice of values for N 1 and N 2 . For example, in the video ofFig. 1, letting N 1 ¼ 1 still provides good results, trading off the ability to image faster temporal changes for a  40% increase in noise.
Likewise, we may also let N 1 ¼ 10 and N 2 ¼ 120, and this likewise
produces reasonable results for this dataset.
A useful metric to use for the expected signal strength of thestriations is the ‘‘thermal contrast” of a pixel, or the average thermal contrast of a scene, given by the temperature difference
between the surface and the ambient air,Fig. 4 shows a scene containing a vertical wood board obliquely
illuminated by the Sun, in which the solar illumination is incident
at an angle of approx. 70° from normal. The wind patterns wereinconsistent in speed and direction, but the wind speed was generally
below 1 m/s. The data was taken on a warm partially-cloudy afternoon, achieving a thermal contrast of DT ¼ 12  C, for ambient air at
70% humidity and at a viewing distance of 7 m. This video illustrates
the complex patterns that the thermal difference images can produce, replicating the swirls and twists created by the advection ofwarm air across the vertical board. At the end of the video sequence,
one can see a burst of air emitted from the narrow tube shown in the
figure, showing rapid cooling from the emitted jet.

DT ¼ T bkg  T air :We generally define good thermal contrast as DT > 15  C, which
can be achieved with many common materials exposed to direct
sunlight.

Video 2

Fig. 5. The temperature analysis of an example pixel over time, showing the 4frame temporal mean temperature of the object T local (black line), the 80-frame
temporal mean T trend (thick gray line), and the central difference between the twodT (red line). (For interpretation of the references to color in this figure legend, thereader is referred to the web version of this article.)

Fig. 5 shows the temperature of a pixel taken from the video
sequence of Fig. 4, giving T local ; T trend , and dT. Dark striations crossing the pixel produce the dips we see in the red curve of Fig. 5, while
the strong dip at about the 35 s point in the video shows the coolingof the surface due to the air burst from the nearby equipment. The
fluctuations indicate striation-induced temperature changes of 10–
20 mK when the pixel thermal contrast is DT  12  C.
Fig. 6 shows a scene taken on a rainy, fully overcast day, so thatthe thermal contrast between air and ground here is much less(DT  5  C). Very light rain is falling on the scene during the video,

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54(a) MWIR filtered image(b) visible image

Fig. 6. The flow visualization a scene of solar-illuminated concrete on a fully overcast day with slight rain (see Video 3 in supplementary data). In the center of the scene ofFig. 6 is a warm bucket filled with toluene (an infrared absorbing gas when vaporized), as well as an electric hot plate (at 120 °C and saturating the camera).

but stops at about halfway through the sequence. The viewing distance to the bucket is 8 m. Despite the poor measurement conditions, the filtered video still allows visualization of the complex
wind motions without difficulty.
An interesting feature of the video in Fig. 6 is that a small 5 mmhigh crack (near the bottom of the MWIR image) across the concrete surface produces enough turbulence to create downstream
striations that are visible in the filtered video.
3. Measurement requirements
The essential requirement for good measurements of wind surface flow is of course to have sufficient signal-to-noise ratio (SNR)
to see the thermal structures in the presence of noise. Larger thermal fluctuations are produced by larger thermal contrast DT
between the object surface and the wind, and these have good visibility (change in emitted radiance) when the surface emissivity ishigh. Thus,  DT can be considered a metric for the relative scale ofthe signal. For outdoor measurements, DT is usually induced
through the absorption of sunlight on a surface, heating it above
the temperature of the ambient air. For Fig. 1, DT was determined
to be about 15 °C using a contact thermistor for the concrete( 50  C) and ambient air ( 35  C), as well as an optical thermometer for the concrete (giving  49  C). For Fig. 4, the temperature of the vertical wooden board was about 47 °C, giving
DT  12  C. Fig. 6 was taken during an overcast skies and very lightrain, so that the thermal contrast was only 5 °C. In addition, thescenes imaged here had wind speeds of 1–2 m/s. Faster windspeeds will generally reduce the thermal contrast between the surfaces in the scene and the ambient air, such that strong wind provides a difficult measurement environment.

In the case of the data shown in Figs. 1, 4, 6, and 9, we know DT
from temperature measurements, and we can estimate the thermal
structure signal by analyzing the temperature history of an example pixel, as shown in Fig. 5. By correlating the structures in thevideo with the pixel thermal history, we can see that the lightand dark striations crossing the pixel generally produce thermalchanges on the order of dT  10 mK, as indicated by the dashedlines in the figure.
To evaluate SNR, we also need a measure of the noise. NETD values are a basic noise measure for thermal cameras, but NETD varies
with the temperature of the surface being imaged. On a hot summer day, sunlit concrete reaches 50 °C, and asphalt pavement canreach temperatures above 60 °C [19]. These elevated temperatures
improve the NETD significantly. If we model the blackbody radiance spectrum of a surface at each temperature, integrated acrossthe detector passband and weighted by the detector responsivity,
we obtain the estimated NETD values for various object temperatures given in Table 2. Because cryo-cooled thermal detectors are
shot-noise limited, we can assume that the noise level scales as a
Poisson distribution from the photon flux.
Another factor to take into account is the reduction of noise due
to summing of multiple frames. The algorithm used for Figs. 1, 4, 6,
and 9 applies moving averages of 4 frames and 80 frames wide.
Assuming stationary object statistics, Table 2 gives the resulting
noise estimates for the moving-average data. By taking the difference of the two moving averages, the resulting noise is roughly thepair summed in quadrature, so that the noise in the resulting
difference-image will be approximately the same as that in the
4-frame moving average. Thus, for imaging a concrete surface at
50 °C, a thermal fluctuation of magnitude 12.5 mK, and a camera
NETD of 6.3 mK, corresponds to an SNR of 2.
In order to verify this result, we constructed a simple simulationof the thermal striations, using elongated Gaussian-shaped features (x-y widths of 1 and 25 pixels) distributed in a semirandom pattern generated by Poisson disk sampling, with an average spacing of 5 pixels. Finally, i.i.d. Gaussian noise is added to theimage to give SNR ¼ 2, with the result shown in Fig. 7. The simulation result bears a good resemblance to the striations visible in the
Table 2Estimated NETD values for the QuazIR SD camera imaging different surface
temperatures, and assuming a surface emissivity of 0.95.

Video 3

Surfacetemperature (°C)

NETD
(mK)

4-frame avg NETD
(mK)

80-frame avg NETD
(mK)

23
30
50
60

15.0
14.3
12.5
11.7

7.5
7.2
6.3
5.9

1.7
1.6
1.4
1.3

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54

no noise

SNR = 1.7

Fig. 7. Simulated thermal striations, showing a noise-free image and an SNR ¼ 2 image.

8x binned

16x binned

Fig. 8. Spatial binning applied to the data of Fig. 1.

wind imaging data, so that the estimated SNR value appears
reasonable.
A second condition for good measurement is on the measurement rate. For the second-derivative filter shown in Fig. 3, thermalchanges in the surface must persist for longer than the ‘‘local” time
– e.g. corresponding to N 1 ¼ 4 frames (66.7 ms) – and should fade
before N 2 ¼ 80 frames (1.3 s). That is, the derivative filter should beproperly designed to fit the underlying scene behavior. That is, thewind flow speeds need to be slow enough that the thermal signature correlation from one frame to the next remains high, so thatwhen we see the change from frame to frame, we understandthe changes as smooth flowing motion rather than sudden jumps.
Using the data of Fig. 1, we can analyze the limiting frame rate of
the camera by subsampling frames from the raw video. Doing this,
we find that the limiting frame rate is coupled to the size of theimage and the smoothness of the flow. For flow containing weakspatial velocity gradients (i.e. predominantly linear motion), one
can understand the motion as smooth flow when the frame-toframe displacement is less than about one fourth of the frame
width. For higher amounts of turbulence (larger spatial velocity
gradients), such as in swirls and rapid changes in wind direction,
the frame-to-frame motion must be smaller than this. Using thedata of Fig. 1 as a guide, we can say that imaging at slow frame
rates of 3.75 frames/s are about the limit for producing usefulvideo at these wind speeds. Imaging a slower rates than this, wefind it difficult to visualize the direction and speed of flow. Conversely, we can say that at our camera’s frame rate of 15 frames/s, the limiting wind speed is probably 4–8 m/s. Imaging faster windspeeds than this requires either a larger field of view or a higher
frame rate.

A third condition for good measurement is on the spatial sampling of the scene. If we change the detector for one with a different pixel size, or the lens for a different focal length, we would liketo know the effect on the visualization. By binning the existing data(again we use the data of Fig. 1), we can simulate conditions ofincreasingly coarse spatial sampling, as shown in Fig. 8. We previously determined that the striations in the original data were about
3 pixels across. While applying binning to this data reduces thenoise, it also blurs together the dark and light streaks, so that theresulting data has weaker contrast. If the individual streaks were
the only signal of interest, then we would expect that 4 binned
data should reduce the signal to negligible levels. However, we
can observe in Fig. 8 and the corresponding video data that spatial
features indicating flow can still be seen in the 8 binned data, andremain apparent though weak even in the 16 binned data. Evidently, what we are observing are not just the independent effectof individual streaks but larger-scale correlations in the flow, suchthat some broad regions of the surface flow are cooler or fasterthan others.
This observation may explain the discrepancy between the
expected and observed spacing between striations mentioned in
Section 1. If the striations we are observing in the images here
are actually bundles averaging multiple individual streaks, eachof which retain a larger correlation to one another than to moredistant features in the image, then we can expect to continue seeing striations even when the spatial resolution is poor.
Finally, we can also mention that objects composed of thermally insulating materials produce stronger striations. Thermallyinsulating surfaces such as concrete allow larger dynamic thermal
perturbations to the surface layer before conduction relaxes them.

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54

However, it is possible to use conducting materials, albeit with
reduced visibility. In one of our tests, we painted a thin sheet ofaluminum with matte paint – a thin layer of about 20 lm thickness, with emissivity 0.95 [20,21]. Taking the aluminum sheet froma cooled indoor environment (22 °C) into the outdoor air (35 °C),
the painted side of the sheet produced easily visible striations
which quickly faded over the course of some tens of seconds while
the sheet reached equilibrium temperature with the surrounding
air. After reaching ambient temperature (35 °C), the sheet nolonger produced visible striations. Thus, conducting materials canbe used as a good background for viewing wind surface flow if
the surface is given a high emissivity coating and if the object substrate is kept significantly hotter or cooler than the ambient air. In
this case, a high thermal conductivity can actually help to induce
the striations by maintaining a strong temperature difference
between the surface and the ambient air.
With each of these criteria, we can assess the circumstances
under which these measurements can be performed with uncooled(longwave infrared) microbolometer cameras instead of the cryocooled midwave infrared cameras. Unlike cryo-cooled cameras,
uncooled cameras have a fixed frame rate (typically 60 frames/s),
a higher NETD (generally  50 mK), and a much wider field ofview (using f =1 lenses instead of the f =2:3 shown here). The wider
field of view means that the camera user must view the scene fromcloser distances, placing a stricter upper limit on measurable windspeeds. The higher NETD and limited frame rate means that thescene must have 3 the thermal contrast for uncooled as for cooled
camera imaging. Taken together, these requirements are rather
strict, so that uncooled cameras are capable of performing thewind visualization measurements only under the most favorable
circumstances: strong direct sunlight with mild wind speeds. This
assessment corresponds with our own direct observations on trying to perform these measurements with microbolometer cameras.
4. Using optical flow to track flow velocity
In addition to qualitative visualization, the video data shown in
Figs. 1, 4, 6, and 9 can also be used as the input to optical flow algorithms in order to quantitatively estimate the transverse flow
velocity ðu; v Þ at each pixel imaging the object surface [22]. One
problem to be careful of is that infrared video data typically has
a much lower SNR than that of visible-camera-based video, so thatmost existing algorithms are not optimized for this low of an SNR.
To compensate for the poor SNR, one can adapt the algorithms toplace more stringent constraints on the smoothness of flow [23].
In addition, the surface flow visualization data satisfies optical flow
algorithms’ ‘‘brightness constancy” assumption only in a roughFig. 9. The wind surface flow estimate from the data of Fig. 1 (see Video 4 insupplementary data).

approximation, so that the estimated velocity vectors will containlarger bias errors than is typical for visible-light imaging.
The result of implementing an optical flow algorithm on thedata of Fig. 1 is shown in Fig. 9, where the point-by-point estimatesof transverse flow direction and speed are indicated with overlay
arrows.

Video 4

5. Conclusion
The passive imaging method shown here provides a way tomeasure turbulent wind patterns near surfaces. Because infrared
imaging requires no preparation of the scene and can be viewed
in real-time, it provides users with a convenient means of visualizing these phenomena over a wide field of view and around complex shapes. Although high-emissivity insulating materials
provide the best backgrounds for measurement, it is also possible
to use painted conducting materials, so that imaging wind flow
around objects such as cars and airplanes is possible. These combined advantages allow thermal engineers, for example, to visualize not just the cooling effect of air flow on surfaces in a complex
facility but also the direction of flow without having to build a
3D fluid model.
While thermal contrast between the surfaces and the wind is arequirement for the method, we have shown that it is possible tocreate visible flow patterns even under unfavorable conditions(overcast sky and rain), so that the method should be widely applicable. Cryo-cooled cameras (either MWIR or LWIR) are the obvious
choice for these measurements due to their good SNR.
Previous research has shown infrared imaging of wind surface
flow, but only in single images, under high thermal contrast conditions, and with very slow wind motion (in terms of pixels perframe) [4]. With cryo-cooled cameras, we show that each of these
conditions can be greatly relaxed, making the technique practical.
In particular, by enabling video measurement, we can capture theinherent dynamics of turbulence.
While our measurements have focused on using wind as thetransparent fluid medium, it should also be possible to use thismethod to image surface flow of any fluid that is transparent in
the thermal infrared region, such as liquid hydrocarbons (such as
methanol, ethanol, and propanol) between wavelengths 45 lm[24,25]. This may be useful for real-time imaging of flow duringliquid cooling in electronic circuits, for example.
One can also reflect that the small thermal perturbations weobserve here place a fundamental limit on the sensitivity of othertechniques used to detect small temperatures changes in thermal
scenes exposed to turbulent air. The recent use of thermal imaging
camera to detect small gas leaks is an example [26,27]. The small

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N. Hagen / Infrared Physics & Technology 87 (2017) 47–54

surface thermal changes induced by wind turbulence witnessed
here will cause problems for algorithms looking for similar amplitude changes induced by the motion of infrared absorbing gas.
Conflict of interest
The author declares no conflict of interest in this work.
Acknowledgments
This research was funded in part from a research grant provided
by Denso Corp.
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