While Infrared light’s short wavelengths better represent the environment’s light, long wavelengths represent the temperature of objects and are not affected by the environment’s light. Thermal imaging systems mainly contain two bands: LWIR 3-5μm and LWIR 7-14μm.

Due to its ability to effectively detect most terrestrial objects, the LWIR band is the most commonly used infrared band. Objects at ambient temperatures emit maximum spectral brightness in the LWIR band and respond very well in the presence of smoke and aerosols. LWIR detectors are not sensitive to sunlight, thus they produce very similar images during both day and night. They create images by depicting emitted heat from objects rather than their visible characteristics.

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Basis of LWIR Imaging

The LWIR (Long Wave Infrared) region is a broad and conductive atmospheric band. Many non-metallic materials exhibit high and relatively constant emissivity in the LWIR band. Examples include most mineral building materials, coating paints, and human skin. In contrast, metals typically have low emissivity and high reflectivity, which vary significantly depending on surface properties.

Temperature measurements captured by a thermal camera consists of emitted, reflected, and transmitted energy, with the measured temperature being the sum of these three energy components. When measuring the temperature of an object, emitted energy can be precisely measured with a calibrated thermography camera.

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Advantages and Disadvantages of LWIR Imaging

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Thermal cameras can detect temperature differences that reveal objects hidden behind other materials, such as beams behind a wall or objects under clothing. LWIR cameras perform well on moonless nights and cloudy days, but their performance on hot days is affected by high atmospheric humidity.

Comparing Thermal Bands

When the temperature of a target closely matches that of the background, distinguishing the target becomes challenging. In such cases, thermal contrast becomes crucial. Thermal contrast is higher in the MWIR (Mid-Wave Infrared) band. At a temperature of 300K, thermal contrast in the MWIR band is around 3.5-4%, whereas in the LWIR (Long-Wave Infrared) band, it is about 1.6%. In LWIR imaging, background noise is lower, and it performs better in mist, fog, and dust compared to the MWIR band. However, MWIR performance can be degraded by sunlight reflections, for instance, sunlight reflected from water can conceal targets in coastal surveillance scenarios. Both bands are adversely affected by fog and rain, but the LWIR band performs better than MWIR in foggy conditions. LWIR is also superior to MWIR for imaging through smoke or aerosols, making it the preferred technology for firefighting applications. LWIR exhibits better response in polluted battlefield conditions as well; for instance, hot targets like burning barrels can quickly saturate an MWIR camera.

Intended Uses of LWIR Imaging

LWIR imaging applications are generally categorized into two types: pure imaging applications and radiometric applications. The first step in choosing the right technology and utilizing it effectively is to accurately define the operating conditions and select the appropriate detector type.

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Types of Detectors

There are two primary types of LWIR detectors: cooled and uncooled detectors. Cooled detectors offer advantages such as high image quality due to high resolution and sensitivity. Uncooled detectors, on the other hand, are more durable and compact. Cooled detectors are typically more sensitive and faster than thermal detectors but require cooling. One of the most attractive features of thermal detectors is their equal response across all wavelengths, contributing to the stability of a system that needs to operate over a wide temperature range.

Microbolometers are particularly suitable for equipment used in military ground operations, such as thermal goggles, scopes, and binoculars used to detect heat sources. They are also commonly used in industrial and security applications for temperature monitoring operations. Additionally, they play an active role in the automotive industry, particularly in advanced driver assistance systems (ADAS) and systems used in autonomous vehicles for detecting obstacles during both day and night.

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Detector Performance

One of the many parameters used to define detector performance is the Noise Equivalent Temperature Difference (NETD), which describes the detector’s ability to distinguish very small differences in thermal radiation. The typical NETD for an uncooled microbolometer is below 50 milliKelvin (mK). Cooled cameras with photon-based detectors can achieve NETD values better than 20mK. Matching the camera’s NETD with the application is crucial for achieving higher temperature sensitivity. The NETD value is also influenced by whether the camera is used with a lens, without a lens, or with spectral filters.

Problems and Solutions

The primary challenge in using microbolometer-based detectors is Fixed Pattern Noise (FPN), which often causes the raw image from the detector to appear noisy. This effect arises because the signal difference per pixel is typically offset-based, making it possible to correct using a simple shutter mechanism. FPN correction, also known as Nonuniformity Correction (NUC), can be performed using various methods, including shutter usage and algorithmic approaches.

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Thermal Imaging Lenses

When selecting an LWIR (Long-Wave Infrared) camera, it is important to understand the field of view and resolution capabilities. Calculating spatial resolution is crucial for obtaining accurate measurements in minimal detail. Thermal camera lenses need to be calibrated according to the sensor. Lenses made from exotic materials such as germanium and zinc selenide are used because common glass types are not transparent to thermal radiation. Infrared lenses are typically athermal to prevent focus shifts. To enhance the sensitivity of uncooled thermal imagers, using lenses with very wide aperture openings, such as ƒ/1.0, can improve the quality of images.

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Key Parameters in Selecting LWIR Camera

Detector resolution and pixel size:

  • NETD value
  • Lens options
  • Power consumption
  • Sizes and weight
  • Interfaces
  • Budget

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