Sensitivity is most often measured by a parameter called Noise Equivalent Temperature Difference or NETD, for example, NETD @ 30 C : 80mK. A Kelvin degree is the SI base unit of thermodynamic temperature equal in magnitude to a degree Celsius, so mK means thousandths of a degree (80mK = 0.080 K).
What is NETD? NETD is defined as the amount of infrared radiation required to produce an output signal equal to the systems own noise. This is a noise rating of the system and should be as low as possible. We are not talking about how loud the system is here!!! We are talking about electronic noise that we translate into a temperature difference at an object temperature of 30 C (86 F).
The kind of noise we are dealing with is called Temporal noise (of or relating to or limited by time). Temporal noise is the time variation in pixel output values under uniform radiation due to device noise.
You can recognize temporal noise as “snow” in an image, best seen when the temperature span is set to a very small value.
Here is an example of temporal noise.
Look familiar? If you look at a dark scene with a camcorder and look at the image, you might see something very similar! The camcorder shows noise at low light levels just like an infrared camera displays it at low temperature levels.
We can graph this noise using a graph called a histogram (a bar chart representing a frequency distribution) which tells us how often certain temperatures appear in the image noise. It looks like this.
Now, if we calculate the standard deviation of the temporal noise, we come up with NETD (area in red).
NETD changes with target temperature. Shown below are two curves, each representing a different temperature range on an infrared camera. You can see that as the object temperature increases, the NETD decreases (better sensitivity). You will also notice that the larger the temperature range, the higher the NETD. The standard for NETD specifications are for an object temperature of 30 C.
- Good image – easy to understand
- Higher efficiency with a better image (you can work in conditions where a less sensitive camera may not find problems)
- Easier to focus the camera
- Easier to identify objects in the IR-image
- More professional looking reports with better images