Figure 226 shows results of the Hyper-Cam viewing the atmosphere at a low elevation angle from MRS on November 16 and 17. The upper left-hand image is that of the uncalibrated radiance when viewing one of the on-board blackbodies. There is a pattern in the instrument response of the focal plane detector array, called the “amoeba” pattern due to its shape. This artifact in the
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spatial characteristics of the signal should not appear in the calibrated signal. However, one can see from the brightness temperature standard deviation plots for November 16 and 17 that this amoeba pattern seems to be present in the calibrated sky scene data, regardless of day or viewing angle. In Figure 226, the upper left panel is the uncalibrated detector output when viewing the ambient calibration blackbody whereas the other three panels show the standard deviation of brightness temperature when viewing the sky at a low elevation angle. The outline of trees can be seen in the field of view; they were fluctuating spatially due to high winds.
Figure 226. Images of “amoeba” pattern in Hyper-Cam radiance for November 16 and 17, 2011.
The temporal variability of the signal was investigated over 14 data cubes (# 8940–8953) from the November 17, 2011, Run 2 dataset. We computed the mean radiance, R0(ν,x,y), by averaging over 14 cubes for each wavenumber, ν, and each image pixel, (x,y). This mean radiance was then subtracted from the actual measured data. The obtained difference, ΔR, is the signal that is being analyzed.
Figure 227 shows the spectrum of the standard deviation of ΔR averaged over all image pixels (320 x 160) for each data cube separately (represented by different colored lines). This figure therefore presents the total variability of the scene, which allows one to detect the spectral channels that vary most during the observation. The first and last 3 channels have been excluded from consideration since they suffer from wrap-around effect (i.e. the measured spectra are not apodized).
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Figure 227. Standard deviation for radiance perturbations averaged over 320 x 160 image pixels, separately for 14 data cubes processed (November 17, 2011, cubes 8940–8953).
Figure 228 shows an image of the standard deviation of ΔR for channel ν = 849.9 cm-1, the channel which showed the highest variability across image pixels. The image looks very similar to the “amoeba” pattern described above and shown in Figure 226. It confirms the necessity to understand the calibration issues.
Figure 228. Standard deviation for radiance perturbations for channel #4 (ν = 849.9 cm-1) that shows the highest variability across image pixels.
The Telops Reveal Pro software that is used to control the Hyper-Cam during a measurement shows a real time update of the infrared image of the scene. As the screen updates, the software cycles through the wavenumber dimension of the hyperspectral data cube and randomly selects a radiance image to show on the display. This amoeba pattern is present on the display during calibration of the sensor when looking at the blackbodies. The presence of the pattern is not of concern here, as it is most likely representative of the detector response and should be removed during calibration. Why then, is it also apparent in the real-time display of the infrared scene?
Further investigation revealed the answer.
The Telops Reveal Pro software does a “quick calibration” on the data cubes for the display. The actual calibration, using the two blackbody measurements to calculate a gain/offset file to calibrate the entire set of measurements, is performed in the separate Telops program Reveal
wavenumber
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0.0000
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Calibrate. This calibration is a post-process step that, for the data taken at MRS, took approximately three weeks to complete. For the most part, the calibrated radiance data obtained from this process does not show these amoeba patterns, and this is confirmed by comparing a blackbody interferogram image to a calibrated radiance image (i.e. the blackbody was measured as though it were the scene). Figure 229 shows the “amoeba” is calibrated out when comparing images of blackbody measurements before and after calibration.
Figure 229. The interferogram image (left panel) and the radiance image (right panel) of the blackbody.
There were occasions where the amoeba pattern was still seen in the radiance data as calibrated by post-processing. As the pattern is essentially the detector response, it should certainly be gone after this more thorough calibration. For the data where these patterns exist in the radiance data, the source of the problem is attributed to a lost calibration. The MRS datasets were taken in thirty minute intervals; blackbody calibrations were performed between each set. Therefore, one must assume that there is no sensor drift or changes in ambient condition during the thirty minutes that the data were taken. The Telops monitored values during a calibration and measurement are shown in Table 12. The data in Table 12 was from cube 8940 of the November 17, Run 2 dataset.
Table 12. Telops monitored values during two blackbody and one scene measurement (all temperatures in Kelvin).
Blackbody 1 Blackbody 2 cube 8940 FTSTemperatureMeasured 301.14 301.15 301.95 IRLensTemperatureMeasured 301.43 301.44 301.42 FPATemperatureMeasured 66.7 66.7 66.75 AmbientTemperature 279.77 279.88 286.86 EntranceWindowTemperature 285.82 285.87 290.46
BeamsplitterTemperature 299.49 299.5 300.6 FTSAmbientTemperature 300.01 300.06 301.38 IRLens1Temperature 301.01 301.02 301.24 CoolerColdFingerTemperature 306.36 306.42 310.77
CoolerCompressorTemperature 321.72 321.72 321.72 IRLensTubeTemperature 302.71 302.73 302.52 EnclosureAmbientTemperature 297.44 297.39 300.98
Ambient RH % 20 20 14
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The two blackbody measurements were used to calibrate cube 8940. Note in Table 12 that there are several instances that indicated the sensor has drifted and the ambient conditions had changed.
Cube 8940 was recorded 44 minutes after the blackbodies, and the calibration was no longer valid. Therefore, if the amoeba patterns are present in calibrated radiance data, the assumption is that it is due to a bad calibration and is not a sensor defect.
This assumption was verified by Christoph Borel of the Air Force Institute of Technology (AFIT) in his presentation entitled, “Data processing and temperature-emissivity separation for tower based imaging Fourier transform spectrometer data” at the 2012 Telops Scientific Workshop.
AFIT uses both the mid-wave and long-wave versions of the Hyper-Cam for testing temperature and emissivity (T&E) separation algorithms and analysis of combustion events, among other things, and for the most part, they do their own calibration rather than use the Telops-provided Reveal Calibrate. Dr. Borel’s presentation on T&E separation included a discussion of calibration, which included calculations of gains and offsets, bad pixel replacement, and flat-field correction. His figures of gain/offset maps were essentially our “amoeba” pattern. AFIT has termed this phenomenon the “Moiré” pattern, and as hypothesized, it represents the non-uniformity of the detector and can be calibrated out by measuring two blackbodies and generating gains and offsets to apply to scene interferograms.