It's tough to trust measurements from instruments when you don't have a clear understanding of how their sensitivity and accuracy are derived, and many times infrared cameras fall in this category. In addition, discussions of infrared camera measurement accuracy typically involve complex terms and jargon that can be confusing and misleading. This can ultimately prompt some researchers to avoid these tools altogether. However, by doing so, they miss out on the potential advantages of thermal measurement for R&D applications. In the following discussion, we strip away the technical terms and explain measurement uncertainty in plain language, providing you with a foundation that will help you understand IR camera calibration and accuracy.

Camera Accuracy Specs and the Uncertainty Equation

Most IR camera data sheets show an accuracy specification such as ±2°C 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. Determining the partial errors, on the other hand, can be tricky.

Figure 1. Standard imaging lens with complex mechanics and an adjustable iris

Partial errors can result from one of several variables in the typical IR camera temperature measurement equation, including:

  • Emissivity

  • Reflected ambient temperature

  • Transmittance

  • Atmosphere temperature

  • Camera response

  • Calibrator (blackbody) temperature accuracy

Once reasonable values are determined for the partial errors for each of these terms, the overall error equation will look like this:

Where ∆T1, ∆T2, ∆T3, etc. are the partial errors of the variables in the measurement equation.

Why do this? It turns out that random errors sometimes add in the same direction, taking you farther from the true value, while other times they add in opposite directions and cancel each other out. Taking the RSS gives you a value that is most appropriate for an overall error specification. This has historically been the specification shown on FLIR camera data sheets.

It's worth mentioning that these calculation are only valid if the camera is being used in the lab or at short range (less than 20 meters) outdoors. Longer ranges will introduce uncertainty in the measurement because of atmospheric absorption and to a lesser extent, its emission. When a camera R&D engineer performs an RSS analysis for almost any modern IR camera system under lab conditions, the resulting number is around ±2°C or 2%—making this a reasonable accuracy rating to use in camera specifications.

Laboratory Measurements and ±1°C or 1% Accuracy

In this section, we take a look at the temperature measurements a camera actually produces when looking at an object of known emissivity and temperature. Such an object is commonly referred to as a blackbody. You may have heard this term before in reference to the theoretical concept of an object with known emissivity and temperature. It is also used to describe a piece of lab equipment that closely emulates this concept. FLIR's calibration lab with a quarter circle of at least 2 cavity blackbodies is shown in Figure 1.

Laboratory measurements of uncertainty involve pointing a calibrated camera at a calibrated blackbody and plotting the temperature over a period of time. Despite careful calibration, there will always be some random error in the measurement. The resulting data set can be quantified for accuracy and precision. Figure 2 demonstrates the results from a calibrated blackbody measurement. The plot shows more than two hours of data from a FLIR A325sc camera looking at a 37°C black-body at a range of 0.3 meter in an indoor environment. The camera recorded the temperature once per second. The data plotted is the average of all pixels in the image. A histogram of this data would make it clearer, but most of the data points were between 36.8°C and 37°C. The widest ranging temperatures recorded were 36.6°C and 37.2°C.

Figure 2. Typical FLIR A325sc camera response when looking at a 37°C blackbody.

Looking at this data, it would be tempting to claim an expected accuracy of 0.5°C for the average of all the pixels — one could even claim ±1°C. However, it could also be argued that since the graph shows an average, it may not be representative of an individual pixel.

One way of knowing how well all of the pixels agree with each other is to look at standard deviation versus time. The graph in Figure 3 shows that the typical standard deviation is less than 0.1°C. The occasional spikes to around 0.2°C are a result of the camera's 1 point update, a type of self calibration procedure that all microbolometer-based cameras must perform periodically.

Figure 3. Standard Deviation of typical A325sc when looking at 37°C blackbody.

So far, we have discussed collecting data from uncooled microbolometer cameras. How will the results differ for a high-performance quantum detector camera?

Figure 4 shows the response of a typical 3-5 μm camera with an Indium Antimonide (InSb) detector, such as the FLIR X6900sc. That camera's documentation shows the accuracy tested at ±2°C or 2%. The graph indicates that the results fall well within those specifications: the accuracy reading on that day was around 0.3°C and the precision reading was around 0.1°C. But why is the offset error at 0.3°C? This could have been caused by the calibration of the blackbody, the calibration of the camera, or any of the partial error terms. Another possibility is that the camera was simply warming up at the beginning of the measurement. If the optics or the inside of the camera body are changing temperature, they may offset the temperature measurement.

Figure 4. Response of a typical InSb camera looking at a 35°C blackbody.

The conclusion we can draw from these two calibration tests is that both microbolometer and photon counting quantum detector cameras can be factory calibrated to provide accuracies of less than 1°C when looking at 37°C objects of known emissivity under typical indoor environmental conditions.

Ambient Temperature Compensation

One of the most critical steps in factory calibrations is ambient temperature compensation. Infrared cameras — whether thermal or quantum detecting — respond to the total infrared energy falling on the detector. If the camera is designed well, most of this energy will be from the scene, and very little from the camera itself. However, it's impossible to completely eliminate the contribution from the materials surrounding the detector and the optical path. Without proper compensation, any changes to the temperature of the camera body or lenses will significantly alter the temperature readings the camera provides.

The best method for achieving ambient temperature compensation is to measure the temperature of the camera and optical path in up to three different locations. The measurement data is then included in the calibration equation. This can ensure accurate readings through the entire range of operating temperatures (typically 15°C to 50°C). This is particularly important for cameras that will be used outdoors or otherwise subjected to temperature swings.

Even with Ambient Temperature Compensation, it's important to allow the camera to fully warm up before making critical measurements. Also, keep the camera and optics out of direct sunlight or other sources of heat. Changing the temperature of the camera and optics will have an adverse effect on measurement uncertainty.

We should note that not all camera makers include ambient temperature compensation in their calibration process. By not properly compensating for ambient temperature drift, the data from these cameras could show significant inaccuracies — as much as 10°C or more. Therefore, be sure to ask about calibrations and how they're performed before investing in an IR camera.