&lt;title&gt;Merlin microbolometer camera calibration&lt;/title&gt;

Green, William J.; Maurer, Donald E.

doi:10.1117/12.439151

Cited by 4 publications

(5 citation statements)

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“…In terms of weight limitations, sUAS (under 25 kg) have only one available radiometric temperature sensor solution: microbolometer infrared sensors (below 200 gr), which have a typical spectral response from ~7 um to ~14 um, using vanadium oxide (VOx) or amorphous silicon (A-Si) as the sensor core [ 31 , 32 , 33 ]. Figure 1 shows a microbolometer camera that is suitable for thermal remote sensing applications, as well as the spectral response of the sensor.…”

Section: Introductionmentioning

confidence: 99%

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Torres‐Rua

2017

Sensors

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In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called “AggieAir”, developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon”) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m2/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m2/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from −5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times.

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Section: Introductionmentioning

confidence: 99%

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Torres‐Rua

2017

Sensors

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“…The out-of-focus blackbody approach was used because it is very difficult to produce a focused, uniform surface temperature of the size necessary for this application [3,4], which, considering the wide field of view (45 o to 60 o ) and 1 m minimum focusing distance of most fire service TICs, would be at least 1 m by 1 m square. The out-of-focus blackbody approach works on the assumption that the blackbody is a Lambertian surface, i.e., its thermal radiance is constant when viewed from any angle.…”

Section: Methodsmentioning

confidence: 99%

Measurement of the nonuniformity of first responder thermal imaging cameras

Lock

Amon

2008

SPIE Proceedings

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Police, firefighters, and emergency medical personnel are examples of first responders that are utilizing thermal imaging cameras in a very practical way every day. However, few performance metrics have been developed to assist first responders in evaluating the performance of thermal imaging technology. This paper describes one possible metric for evaluating the nonuniformity of thermal imaging cameras. Several commercially available uncooled focal plane array cameras were examined. Because of proprietary property issues, each camera was considered a 'black box'. In these experiments, an extended area black body (18 cm square) was placed very close to the objective lens of the thermal imaging camera. The resultant video output from the camera was digitized at a resolution of 640x480 pixels and a grayscale depth of 10 bits. The nonuniformity was calculated using the standard deviation of the digitized image pixel intensities divided by the mean of those pixel intensities. This procedure was repeated for each camera at several blackbody temperatures in the range from 30º C to 260º C. It has observed that the nonuniformity initially increases with temperature, then asymptotically approaches a maximum value. Nonuniformity is also applied to the calculation of Spatial Frequency Response as well providing a noise floor. The testing procedures described herein are being developed as part of a suite of tests to be incorporated into a performance standard covering thermal imaging cameras for first responders.

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“…A shift in the MTF curve would indicate that the performance of an imager had changed, but not necessarily the cause. The MTF can be derived from a direct measurement of the line spread function or it can be obtained by differentiation of the edge spread function (ESF) 8 . The latter method was used to obtain the MTF using a commercially available analysis code 10 , SFRMAT3.…”

Section: Mtf Proceduresmentioning

confidence: 99%

High-g launch testing of a low-cost un-cooled LWIR imager

et al. 2014

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Unmanned aerial vehicles (UAVs) and smart munitions require low-cost IR sensors that fit within very small volumes, yet offer acceptable performance and landing/launch survivability. The LWIR band provides unique contrast for specific applications in both UAVs and smart munitions, with smart munitions presenting an additional challenge of high g-loads during launch. These high g-loads are not typically a design target of low-cost, un-cooled commercial off the shelf (COTS) LWIR sensors. This work addresses the challenges of adapting a COTS un-cooled LWIR imager for launch survivability. The sensor was modeled for mechanical stability and weaknesses identified. Modifications were made to improve launch survivability and multiple units were tested. Data is presented on the optical performance as measured through the modulation transfer function (MTF) both before and after launches for multiple locations across the lens.

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<title>Merlin microbolometer camera calibration</title>

Cited by 4 publications

References 0 publications

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature

Measurement of the nonuniformity of first responder thermal imaging cameras

High-g launch testing of a low-cost un-cooled LWIR imager

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