Non-uniformity correction and bad pixel replacement on LWIR and MWIR images

Mudau, Azwitamisi E.; Willers, Cornelius J.; Griffith, D.; Roux, Francois P. J. le

doi:10.1109/siecpc.2011.5876937

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…The most commonly used approach to compensate for thermal drift is shutter based [28,30]. Others have used a contact sensor between the detector matrix and the lens [31] or other locations inside the sensor [32], as well as "blind" pixels whose signal does not depend on the radiation of the observed scene [33].…”

Section: Introductionmentioning

confidence: 99%

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

et al. 2018

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The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 • C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1 • C compared to those where no drift correction was applied.

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Section: Introductionmentioning

confidence: 99%

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

et al. 2018

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“…Calibration-based methods include single point correction (SPC), two point correction (TPC), multiple point correction (also known as piecewise linear correction) and the improved two point correction algorithm. In these methods, the gain and offset parameters are estimated by exposing the FPA to different uniform blackbody temperatures depending on the algorithm adopted [5]. In multiple point correction the detector is exposed to several black bodies at different temperatures to divide nonlinear characteristic to piece-wise linear sections.…”

Section: Procedures Of a Non-uniformity Correctionmentioning

confidence: 99%

“…In multiple point correction the detector is exposed to several black bodies at different temperatures to divide nonlinear characteristic to piece-wise linear sections. This approach requires a significant increase in the capability of the NUC electronics and a precise calibration source [5]. The next group of non-uniformity correction is a group of scene-based algorithms.…”

Section: Procedures Of a Non-uniformity Correctionmentioning

confidence: 99%

Non-uniformity correction with temperature influence compensation in microbolometer detector

Krupiński

Bieszczad

Gogler

et al. 2015

Image Sensing Technologies: Materials, Devices, Systems, and Applications II

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Because of a significant impact of the microbolometer array temperature on the infrared image quality, it is necessary to compensate the influence of the temperature on the NUC process. In the most common applications two approaches are used: the first is a stabilization of the microbolometer array temperature by a thermoelectric cooler, the second is updating correction coefficients obtained from reference source, for example a shutter [14]. Both of the most common approaches have theirs disadvantages. The first case needs a considerable amount of energy for temperature stabilisation. The second one needs a reference target and a mechanical procedure to place the target at the front of the detector. Additionally, during calibration the reference target is blocking radiation from the scene, thus interrupting measurements with the thermal camera. In the article a non-uniformity correction method is presented which allows to compensate for the influence of detector's temperature drift. For this purpose, dependency between output signal value and the temperature of the detector array was investigated. Additionally the influence of the temperature on the Offset and Gain coefficients was measured. Presented method utilizes estimated dependency between output signal of detectors and their temperature. In the presented method, the dependency between output signal value and the temperature of the detector is estimated during time of starting detector. The coefficients are estimated for every pixel. In the article proposed method allows to compensate the influence of detectors temperature fluctuation and increase a time between shutter actuation process.

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“…Calibration-based methods include a single point correction (SPC), a two point correction (TPC), a multiple point correction (also known as a piecewise linear correction), and an improved two point correction algorithm. In these methods, the gain and offset parameters are estimated by exposing the FPA to different uniform blackbody temperatures depending on the algorithm adopted [5]. In a multiple point correction the detector is exposed to several black bodies at different temperatures to divide nonlinear characteristic to piece-wise linear sections.…”

Section: Procedures Of a Non-uniformity Correctionmentioning

confidence: 99%

“…In a multiple point correction the detector is exposed to several black bodies at different temperatures to divide nonlinear characteristic to piece-wise linear sections. This approach requires a significant increase in the capability of the NUC electronics and a precise calibration source [3,5].…”

Section: Procedures Of a Non-uniformity Correctionmentioning

confidence: 99%

Non-Uniformity Correction in Microbolometer Array with Temperature Influence Compensation

Krupiński¹,

Bieszczad²,

Sosnowski³

et al. 2014

Metrology and Measurement Systems

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In the article a non-uniformity correction method is presented which allows to compensate for the influence of detector's temperature drift. For this purpose, dependency between output signal value and the temperature of the detector array was investigated. Additionally the influence of the temperature on the Offset and Gain coefficients was measured. Presented method utilizes estimated dependency between output signal of detectors and their temperature. In the presented method, the shutter is used for establishing signal reference. Thermoelectric cooler is used for changing the temperature of the detector array.

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Non-uniformity correction and bad pixel replacement on LWIR and MWIR images

Cited by 22 publications

References 8 publications

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

Non-uniformity correction with temperature influence compensation in microbolometer detector

Non-Uniformity Correction in Microbolometer Array with Temperature Influence Compensation

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