How Does Thermal Imaging Work?

By Owais AliReviewed by Lexie CornerApr 23 2025

Thermal imaging is the process of capturing and visualizing heat emitted by objects. It allows users to see temperature differences without physical contact, making it valuable in fields like maintenance, construction, medicine, and security. But how does it actually work, and how accurate is it?

This article explains the basics of thermal imaging, the types of cameras used, and the key factors that influence results.

Image Credit: rebinworkshop/Shutterstock.com

What Is Thermal Imaging?

Thermal imaging is a non-invasive technology that detects and visualizes thermal energy (infrared radiation) emitted by objects.

Unlike conventional imaging systems that rely on visible light, thermal imaging sensors operate within the infrared spectrum to capture variations in surface temperature. This allows users to identify heat patterns and temperature anomalies that are imperceptible to the human eye.

A thermal imaging device converts emitted infrared radiation into a visible image, displaying temperature distributions through color gradients. For example, cooler areas may appear blue, while warmer regions often show as red or yellow, enabling precise interpretation of surface temperature characteristics.

Because it allows for non-contact detection of heat signatures, thermal imaging is essential for diagnostics and monitoring across a wide range of industries.

In industrial settings, it supports predictive maintenance by identifying thermal anomalies in machinery and electrical systems. In construction, it helps detect insulation defects, moisture ingress, and heat loss. Its ability to visualize temperature variations in real time also makes it a critical tool for security, medical diagnostics, and emergency response applications.^1,2

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Working Principle

Thermal imaging operates on the principle of radiative heat transfer. All objects with a temperature above absolute zero emit infrared radiation, the intensity of which is proportional to the object’s thermal energy. This emission is described by the Stefan–Boltzmann Law, which quantifies the relationship between an object’s temperature and the amount of radiant energy it emits.

According to the law, the total radiative power (P) emitted by an object is expressed as P = εσAT⁴, where:

• ε is the emissivity of the material (a dimensionless value between 0 and 1, representing how efficiently a surface emits thermal radiation)
• σ is the Stefan-Boltzmann constant (5.67 × 10⁻⁸ W·m⁻²·K⁻⁴)
• A is the surface area of the object
• T is its absolute temperature in Kelvin

The fourth-power dependence on temperature allows thermal imagers to detect even small thermal gradients with high sensitivity, making it possible to identify subtle temperature variations across surfaces.^3,4

Detection Process

An infrared camera or imager captures the infrared radiation emitted by objects and focuses it onto a focal plane array (FPA) sensor using an optical system composed of lenses and mirrors. The FPA consists of individual detector elements, such as microbolometers or photodiodes, which convert the absorbed infrared energy into electrical signals.

These electrical signals vary in magnitude depending on the intensity of the incident infrared radiation and are then digitized by a signal processing unit. This unit applies image processing algorithms to convert the raw data into a thermal image, where temperature variations are displayed using a color palette, typically showing warmer areas in yellow or orange, and cooler regions in blue or purple.

Advanced systems may enhance these images further by integrating visible light data or using technologies such as IR-Fusion, which provides a more comprehensive view and helps accurately localize temperature-related anomalies.^1,5

Types of Thermal Imagers

Thermal imagers are generally classified into two main categories: cooled and uncooled systems.

Cooled Thermal Imagers

Cooled thermal imagers are cryogenically maintained at temperatures as low as -150 °C to reduce thermal noise and prevent sensor self-emission. This enables them to detect minute temperature differences down to 0.02 °C. They offer superior spatial resolution and faster frame rates, allowing accurate imaging of fast-moving objects, as shown in high-speed captures of rotating tires.

Despite their sensitivity and long-range capability, these systems are bulky, require time to cool during startup, consume more power, and are expensive for general use, making them suitable for specialized applications such as defense, aerospace, and scientific R&D.

Uncooled Thermal Imagers

Uncooled thermal imagers operate at ambient temperatures using microbolometer-based sensors, eliminating the need for cryogenic cooling. These systems are compact, energy-efficient, and can detect temperature differences as low as 0.2°C.

They typically feature noise-equivalent temperature difference (NETD) values below 50 mK and resolutions up to 640 × 480 pixels with 17 μm pitch detectors.

While they lack the sensitivity and spatial resolution of cooled systems, they are more robust, affordable, and maintenance-free, making them ideal for industrial inspections, security monitoring, and mobile applications.^6,7

Table 1 Comparison of Thermal Imagers from Leading Manufacturers

Factors Influencing Accuracy

The accuracy of thermal imaging is affected by several factors that must be carefully managed to ensure precise temperature measurement and reliable data interpretation.

1. Atmospheric Interference

Thermal imaging accuracy is reduced by atmospheric attenuation caused by dust, mist, smoke, and gas absorption, particularly from water vapor and carbon dioxide. High humidity (e.g., >75 %) and rain diminish signal strength, while direct sunlight can artificially elevate surface temperatures by several degrees due to added emission and reflection.

2. Instrumental Errors

Thermal imagers are subject to inherent measurement errors caused by sensor noise, calibration drift, and systematic offsets. These errors can introduce baseline deviations in temperature readings and necessitate regular calibration, compensation algorithms, and the use of high-sensitivity detectors to maintain measurement accuracy.

3. Emissivity-Related Errors

Temperature readings are skewed by emissivity variations, especially on low-emissivity surfaces such as polished metals (ε < 0.10), which reflect ambient radiation. In contrast, high-emissivity materials like water or human skin (ε ≈ 0.98) yield more accurate results, and corrective measures like surface coatings or emissivity adjustments are required for precise measurements.

4. Geometric and Viewing Angle Effects

Viewing surfaces at oblique angles reduces apparent emissivity and distorts thermal readings, particularly in industrial environments with angled surfaces. The source-size effect further introduces error when the target does not fully occupy the sensor's field of view, resulting in pixel-level averaging with cooler or warmer backgrounds.

5. Distance and Spatial Resolution Effects

As viewing distance increases, spatial resolution decreases, which can lead to temperature averaging and obscure localized hot spots. This is particularly important in long-range applications, such as rotary kiln monitoring, where reduced pixel coverage and atmospheric attenuation jointly decrease measurement precision.⁸

Conclusion

Thermal imaging technology enables precise, non-contact visualization of temperature gradients and anomalies across diverse applications. However, its effectiveness depends on careful consideration of emissivity, ambient conditions, viewing geometry, and sensor specifications. Understanding these factors is essential for properly implementing and interpreting thermal imaging data.

As thermal imaging technology advances, it will become an indispensable tool for data-driven decision-making across various fields.

For a detailed walkthrough of thermal imaging, watch:

For more information on the applications of thermal imaging and the latest advances in infrared sensing, materials, and system integration, please visit:

• Advancing Thermal Imaging Technology Using Spinning Metasurfaces
• Thermal Imaging for Wildlife Monitoring and Conservation
• Thermal Imaging Sensors: Enhancing Industrial Safety and More
• How is Thermal Imaging Used in Climate Change Research?

References and Further Readings

1. Fluke. (2025). What is Thermal Imaging? How a Thermal Image is Captured. [Online]. https://www.fluke.com/en/learn/blog/thermal-imaging/how-infrared-cameras-work
2. Strickland, C. (Ed.). (2017). Thermal Imaging: Types, Advancements and Applications. Nova Science Publishers, Incorporated. https://novapublishers.com/shop/thermal-imaging-types-advancements-and-applications/
3. Short, D. B. (2012). Thermal imaging in the science classroom. School Science Review, 94(346), 75-78. https://www.researchgate.net/publication/236587391_Thermal_imaging_in_the_science_classroom
4. US DOE - Engineering Library. (2025). Radiant Heat Transfer. [Online]. https://engineeringlibrary.org/reference/radiant-heat-transfer-doe-handbook
5. Vollmer, M. (2021). Infrared thermal imaging. In Computer vision: A reference guide (pp. 666-670). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-63416-2_844
6. Lynred. (2019). Seeing the invisible: cooled vs. uncooled thermal imagers. [Online]. https://www.lynred.com/blog/seeing-invisible-cooled-vs-uncooled-thermal-imagers
7. Teledyne FLIR. (2023). Cooled or Uncooled? [Online]. https://www.flir.com/discover/rd-science/cooled-or-uncooled/?srsltid=AfmBOooXTfxUl-4wI5rws9kz-Jp_Rew8mXgU8TG1Weibf6pFIOeGhhpm
8. Pan, D., Mo, T., Jiang, Z., Duan, Y., Maldague, X., & Gui, W. (2025). Interference Factors and Compensation Methods when Using Infrared Thermography for Temperature Measurement: A Review. arXiv preprint arXiv:2502.17525. https://doi.org/10.48550/arXiv.2502.17525

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