Read-Noise Characterization of Focal Plane Array Detectors via Mean-Variance Analysis

Sperline, Roger P.; Knight, Andrew K.; Gresham, Christopher A.; Koppenaal, David W.; Hieftje, Gary M.; Denton, M. Bonner

doi:10.1366/000370205774783250

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…6,10,11 This paper utilizes the radiometric calibration data to form an empirical estimate of the variance gain and offset at each pixel. 12 The spectral gain and offset of Results reflect a sharp increase in noise variance towards the longer wavelengths of the operating range. This is due to the low sensor responsivity at these wavelengths.…”

Section: Signal Modelmentioning

confidence: 94%

Incorporating signal-dependent noise for hyperspectral target detection

Morman

Meola

2015

SPIE Proceedings

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The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

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Section: Signal Modelmentioning

confidence: 94%

Incorporating signal-dependent noise for hyperspectral target detection

Morman

Meola

2015

SPIE Proceedings

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“…The noise levels from these three sources were experimentally determined in the same fashion as described in our previous study 9 [23]. Specifically, the photon transfer curve of the ICCD detector was used to determine both the detector-read and photon-shot noise [24]. The source-flicker noise level was characterized from experimentally acquired LAMIS measurements of the C 2 Swan band.…”

Section: Characterization Of Measurement Noisementioning

confidence: 99%

Reduction of spectral interferences and noise effects in laser ablation molecular isotopic spectrometry with partial least square regression – a computer simulation study

Mao

Chan

Zorba

et al. 2016

Spectrochimica Acta Part B: Atomic Spectroscopy

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The fundamental analytical accuracies and precisions attainable by laser ablation molecular isotopic spectrometry (LAMIS), with emphasis on the impacts from spectral interferences and measurement noise, were investigated by means of computer simulation. The study focused on the analysis of a minor isotope at sub-to single-percentage abundance level. With a natural abundance about 1.1% for 13 C, the C 2 Swan band (d 3 Π ga 3 Π u) with  = +1 was selected as a representative system. The characteristics (e.g., noise amplitude and distribution, signal strength, and signal-to-background ratio) of the simulated spectra were experimentally characterized. Partial least square (PLS) regression was used to extract isotopic information from the simulated molecular spectra. In the absence of any spectral interference and with the use of a calibration set consisting of eleven isotopic standards, the theoretical accuracies and precisions with signal accumulation from 100 laser shots are about 0.002% and 0.001%, respectively, in absolute percentage abundance of 13 C. The theoretical analytical accuracies slightly degrade, but are adequate for many applications, to 0.004% and 0.008% respectively, for calibrations involving only three and two isotopic standards. It was found that PLS regression is not only immune to both source-flicker and photon-shot noise, but is also effective in differentiating the spectral patterns from the analyte against those from spectral interferences. The influences of spectral interference from single or multiple atomic emission lines were simulated, and new ways to minimize their impacts were formulated and demonstrated. It was found that the wavelength range selected for the computation of the normalization factor should not contain any spectralinterfering peak, and a properly chosen wavelength range increases the tolerance of spectral interference by at least one order of magnitude. With matrix-matched calibration standards, the precisions (expressed as RSDs of the determined 13 C isotopic abundances) degrade from ~1‰ in 2 the absence of spectral interference, to ~3‰, 10‰ and 20‰ with spectral-interfering peaks that are 10, 100 and 1000, respectively, stronger than the molecular bandhead of the analyte. The study concluded that PLS regression is a powerful and indispensable tool for extraction of isotopic information from LAMIS spectra.

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“…However, using concepts from statistical optics the behavior of a particular data set following application of gain via multiplication or division can be determined. The method of mean-variance used to calibrate the gain of charge coupled devices (CCDs) described by Mortara and Fowler relies on the Poisson statistics of photons and is widely used to characterize the gain of imaging detectors [23] , [24] . The mean-variance plot reveals the number of photons per arbitrary unit of readout allowing light intensity to be compared across systems and gain settings.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental pre-exponential factors can provide information about the detector and the process of detection. One example is the determination of multiplicative gain, G , applied to a system [23] , [24] . A perfect Poisson process ( α = a = 1) will produce a mean-variance plot with a slope of one ( ).…”

Section: Introductionmentioning

confidence: 99%

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Fluctuation Scaling, Taylor’s Law, and Crime

et al. 2014

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Fluctuation scaling relationships have been observed in a wide range of processes ranging from internet router traffic to measles cases. Taylor’s law is one such scaling relationship and has been widely applied in ecology to understand communities including trees, birds, human populations, and insects. We show that monthly crime reports in the UK show complex fluctuation scaling which can be approximated by Taylor’s law relationships corresponding to local policing neighborhoods and larger regional and countrywide scales. Regression models applied to local scale data from Derbyshire and Nottinghamshire found that different categories of crime exhibited different scaling exponents with no significant difference between the two regions. On this scale, violence reports were close to a Poisson distribution (α = 1.057±0.026) while burglary exhibited a greater exponent (α = 1.292±0.029) indicative of temporal clustering. These two regions exhibited significantly different pre-exponential factors for the categories of anti-social behavior and burglary indicating that local variations in crime reports can be assessed using fluctuation scaling methods. At regional and countrywide scales, all categories exhibited scaling behavior indicative of temporal clustering evidenced by Taylor’s law exponents from 1.43±0.12 (Drugs) to 2.094±0081 (Other Crimes). Investigating crime behavior via fluctuation scaling gives insight beyond that of raw numbers and is unique in reporting on all processes contributing to the observed variance and is either robust to or exhibits signs of many types of data manipulation.

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Read-Noise Characterization of Focal Plane Array Detectors via Mean-Variance Analysis

Cited by 13 publications

References 9 publications

Incorporating signal-dependent noise for hyperspectral target detection

Incorporating signal-dependent noise for hyperspectral target detection

Reduction of spectral interferences and noise effects in laser ablation molecular isotopic spectrometry with partial least square regression – a computer simulation study

Fluctuation Scaling, Taylor’s Law, and Crime

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