A Comprehensive Breakdown of What ‘The Commercial UAV Show 2016’ Had to Offer Search and Rescue – Part 4


Please note: the technical term for drones – ‘unmanned aerial vehicles (UAVs)’ – will be used throughout this blog post.

Before ‘The Battle of The Thermals’ commences, this section offers a coherent breakdown of the aspects of a thermal imaging camera that translate into a better performance: a Thermal Cameras 101, if you will. Hopefully it will bring clarity to the lists of thermal specifications that were handed out at the event.

Unfortunately, simply comparing manufacturer’s specification sheets won’t provide the information needed to get the most effective thermal imagers for your money. An aerial thermal camera offers vast opportunities to the search and rescue (SAR) community, but only when the correct one is chosen.

It’s tough to trust measurements and specifications when you don’t have a clear understanding of what these calculations mean for a product’s performance, and thermal cameras commonly fall into this category. Additionally, discussions of thermal cameras typically involve complex terms and jargon that can be confusing and misleading if unfamiliar with the terms used. In the following discussion, I strip away the technical terms and explain the key performance criteria of a thermal camera in plain language, providing a foundation that will help with understanding the mountain of IR literature handed out at The Commercial UAV Show 2016. This section is followed by a comparison between the thermal cameras that were on display at the show, dubbed The Battle of The Thermals.


Thermal Overview Title.png

Firstly, infrared wavelengths are too long for the human eye to detect; it is the part of the electromagnetic spectrum that is perceived as heat. Infrared is split into categories based on wavelength: near-infrared, short-wavelength infrared, mid-wavelength infrared, long-wavelength infrared, and far infrared. The “thermal imaging” region usually concerns the latter two types.

All objects that have a temperature above zero emit heat. It is this emittance that thermal cameras can detect. Plus, thermal imagers are usually radiometric, meaning they measure and store the temperatures that they’ve detected at every point in an image.

There are five main specifications that are critical in the process of selecting a thermal imager: detector resolution, thermal sensitivity (NETD), temperature effective range, pixel pitch, and accuracy. All terms will be discussed.



The detector is the heart of any thermal camera, similar to the role of the CCD detector chip in a standard video camera. It’s the component that gathers the infrared energy and transforms that data into an image.

Detector resolution plays a pivotal role in image quality of thermal imaging cameras. Higher resolutions provide precise and reliable measurements of smaller targets from further distances, creating sharper thermal images. The higher the detector resolution, the more accurate the camera. The low-end options for thermal resolution typically offer the 160×120 detector or the 320×240 detector formats, but the standard is quickly becoming the 640×480 detector.

To contextualise this, if a camera with 640×480 resolution was compared against a camera with 320×240 resolution that both use the same size of lens, you’ll find that the 640’s angular field of view (FoV) will actually be wider, yet will also detect objects from farther away. In the real world, this means that it can cover a wider search area and still be able to detect objects from a greater distance. Hence its importance to SAR operations, the tactical benefits of increased detector resolution are measurable and undeniable.

Moreover, when evaluating between detector resolution and display resolution, be aware that the quality of the thermal image and its data is always determined by the detector resolution. For example, if the built-in screen has a resolution of 307,200 pixels (640×480) but the thermal detector resolution is only 19,200 pixels (160×120), the thermal image can only be measured by the resolution of the thermal detector.

The examples below show that as the thermal detector resolution increases, the image detail becomes clearer and the temperature at a single point is more accurate.




Thermal sensitivity is most often measured by a parameter called Noise Equivalent Temperature Difference (NETD), which measures the smallest temperature difference that a thermal imaging camera can detect.

Thermal sensitivity is measured in milliKelvins (mK). Cameras are more sensitive with values at the low end of the scale. For example, cameras with 50mK are about 4 times as sensitive as a camera with 200mK. The more sensitive (50mK) cameras provide a wider temperature difference, resulting in more colours on the thermal display.

Sensitivity expresses the ability of an infrared camera to display a very good image even if the thermal contrast in a scene is low. Put another way, a camera with good sensitivity can distinguish objects in a scene that have very little temperature difference between them.

If you take a low-light photo with your mobile phone and look at the image, you might see the “snow” effect, or – more technically – noise effect. Your phone shows noise at low light levels just like a thermal camera displays it at low temperature levels. NETD plots this overall noise and extracts the standard deviation, creating less “noisy” feedback of temperatures. As Thomas Sylvest, Watch Commander at Copenhagen Fire & Rescue Service, expressed during his presentation at the show, too much data can be detrimental to SAR agencies as it can cause information overload. NETD narrows the amount of temperature data acquired by removing the “noise” and giving a more accurate feedback value.

To put it simply, cameras with a low NETD will detect smaller differences and provide higher resolution images with increased accuracy. An example where this would be especially useful is persons in the water incidents; given the fact skin temperature rapidly decreases to match the water temperature, only the heat emitting from the top of the head will be vaguely traceable with a minute temperature difference for detection. A low NETD is ideal for these incidents. Structure fires on the other hand, can cope with a higher NETD thermal camera, which are usually more cost effective.



When selecting a thermal imager, it is important to evaluate the temperature range that will be suitable for your applications. For example, Fire and Rescue Services would need a thermal camera that features a wider temperature range to accommodate their practices that involve high temperatures. A thermal camera with a limited temperature range could suffer from a “white out” when exposed to high temperatures – very similar to an overexposed photograph; you simply lose all definition and detail.

Secondly, the higher the temperature range, the higher the NETD. A higher temperature range and higher NETD will certainly add to information overload, ultimately being counterintuitive. This is why a temperature gauge is a vital characteristic for a SAR thermal camera to have. This feature allows you to set temperature parameters; narrowing the feedback to the temperature of the objects you’re searching for, rather than receiving the thermal camera’s full spectrum of detectable emittance. The photo below was taken at the show and depicts the WIRIS 640’s temperature parameters setting, or “isotherms” as they have named it. It clearly shows how the integration of a temperature gauge can help select people without giving all temperature values of objects in a frame.




The detector’s pixel pitch is also an important piece to the puzzle in predicting image quality and range performance. It is typically measured in micrometres (μ), or “microns”. When looking at a camera’s pixel pitch, essentially the lower the numbers, the better – the smaller the pixel pitch, the more image detail you’ll get in a smaller package.

Let’s compare two hypothetical uncooled VOx cameras both with 100mm f/1.6 lenses; one camera has 25-micron pixels, and the other has 17-micron pixels. All other things being equal, the 17-micron camera will detect a person from over 22% farther away than the camera with 25-micron pixels.

Just as important, however, the 17-micron camera will also produce more detailed, higher contrast images that will get better results from analytics and software packages.

Thermal crossovers further reinforce the importance of a smaller pixel pitch. A thermal crossover happens at one of two moments throughout a 24-hour cycle. Essentially, there are two times of the day in which items within an image are most likely to have the smallest differences in temperature (isothermal): just before the sun goes down, when things have been soaking up the sun’s rays all day and have reached a critical point of solar loading, and in the middle of the night, when everything has radiated off its stored energy and cooled to a similar degree. Both conditions are called points of “thermal crossover”. A higher pixel pitch will suffer noticeably during these timeframes; producing flat, washed-out images, which are difficult to distinguish and to draw out important information from. You cannot have your unmanned aerial vehicle (UAV) search asset as ineffective during the most difficult search hours, thus a lower pixel pitch is important.



Most IR camera datasheets show an accuracy specification such at +/-2oC or 2% of the reading. This specification is the result of a widely used uncertainty analysis technique called “Root-Sum-of-Squares”, or RSS. The idea is to calculate the partial errors for each variable of the temperature measurement equation, square each error term, add them all together, and take the square root. While this equation sounds complex, it’s fairly straightforward. In short, the RSS uncertainty analysis technique allows us to determine the accuracy of infrared cameras, and this “+/-2oC” value represents its margin of error.



One final term that is useful for selecting the right thermal camera for SAR is “picture-in-picture (PiP) imaging”. PiP combines thermal and visible-light images by placing a “framed” thermal image over its corresponding visible-light photo. This allows you to have the radiometric data of a thermal camera overlaying a digital photographic image, like a digital camera, to give reference to the environment being measured. This will bring more clarity to the feedback you are observing, reducing information overload.


  • The higher the detection resolution, the more accurate the camera
  • The lower the NETD, the better the sensitivity
  • The larger the temperature range, the higher the NETD
  • The smaller the pixel pitch, the more image detail you’ll get
  • The lower the accuracy temperature/percentage specification, the lower the margin of error

Got it? Great. Let The Battle of the Thermals begin!

Next Section ->

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If you have anything to add or questions to ask or recommendations for future research blog posts, please don’t hesitate to use the comment section below. AND don’t forget to email subscribe, so you’re always up-to-date with the world of SAR UAVs!


All references are located on the Introduction page.


All image sources are located on the Introduction page.

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