Test stand for determining parameters of microbolometer camera

Krupiński, Michał; Bareła, Jaroslaw; Kastek, M.; Chmielewski, Krzysztof

doi:10.1117/12.2241073

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The main difference is the lack of "cooling" mechanisms to maintain the sensor at a constant operating temperature. Microbolometer cameras require additional procedures to stabilize the operation conditions before and during use, thus avoiding "drift" conditions that are difficult to correct/adjust after data collection [15][16][17] . Furthermore, infrared sensors and microbolometer cameras have, by design, different spectral characteristics [18][19][20] , making them distinct from the spectral characteristics from scientific satellite missions (Landsat, MODIS, ASTER, ECOSTRESS) [21][22][23][24][25] as seen in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Torres‐Rua

Aboutalebi

Wright

et al. 2019

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV

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Microbolometer thermal cameras in UAVs and manned aircraft allow for the acquisition of highresolution temperature data, which, along with optical reflectance, contributes to monitoring and modeling of agricultural and natural environments. Furthermore, these temperature measurements have facilitated the development of advanced models of crop water stress and evapotranspiration in precision agriculture and heat fluxes exchanges in small river streams and corridors. Microbolometer cameras capture thermal information at blackbody or radiometric settings (narrowband emissivity equates to unity). While it is customary that the modeler uses assumed emissivity values (e.g. 0.99-0.96 for agricultural and environmental settings); some applications (e.g. Vegetation Health Index), and complex models such as energy balance-based models (e.g. evapotranspiration) could benefit from spatial estimates of surface emissivity for true or kinetic temperature mapping. In that regard, this work presents an analysis of the spectral characteristics of a microbolometer camera with regard to emissivity, along with a methodology to infer thermal emissivity spatially based on the spectral 1 *

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Section: Introductionmentioning

confidence: 99%

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Torres‐Rua

Aboutalebi

Wright

et al. 2019

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV

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show abstract

“…Many studies and techniques to eliminate FPN have been made and the non-uniformity correction technique (NUC) has been created. In this case, the matrix is subjected to uniform and constant radiation and the response given by each sensor is adjusted [1], [7], [8]. Doing this, each pixel in the matrix is separately analyzed.…”

Section: A Calibration and Nuc Processmentioning

confidence: 99%

Non-uniformity correction in a Long Wave Infrared Focal Plane Array as a calibration temperature function

Santos¹,

Vieira

Castro

2019

JICS

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Despite of the manufactory process, all detector or pixel in a Focal Plane Array (FPA) has a different responsivity and offset when evaluated. These variations result in a specific kind of noise, called Fixed Pattern Noise (FPN) or spatial noise. During the image processing, those pixels who has a large deviation in responsivity or in their noise are considered bad pixels, and have their influence removed during the exhibition of a scene. Knowing the answer of all those pixels are the first test procedure in an FPA characterization. However, during the Non-uniformity correction and bad pixel replacement it is necessary to choose two reference temperatures, calibration points, and those temperature affects important parameters such as Uniformity, Noise Equivalent Temperature Difference (NETD) and Signal Transfer Function (SiTF). This work will study the calibration temperature influence on the Non-Uniformity Correction (NUC) quality by comparing uniformity, bad pixel number, NETD and SiTF values during characterizations.

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Test stand for determining parameters of microbolometer camera

Cited by 2 publications

References 2 publications

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Estimation of surface thermal emissivity in a vineyard for UAV microbolometer thermal cameras using NASA HyTES hyperspectral thermal, and landsat and AggieAir optical data

Non-uniformity correction in a Long Wave Infrared Focal Plane Array as a calibration temperature function

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