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Grey level and noise evaluation of a Foveon X3 image sensor: a statistical and experimental approach.

Riutort-Mayol G, Marqués-Mateu A, Seguí AE, Lerma JL - Sensors (Basel) (2012)

Bottom Line: A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper.The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time.It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Valencia 46022, Spain. gabriuma@upv.es

ABSTRACT
Radiometric values on digital imagery are affected by several sources of uncertainty. A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper. The procedure is performed on the grey level output signal using image raw units with digital numbers ranging from 0 to 2(12)-1. The procedure is entirely based on statistical and experimental techniques. Design of Experiments (DoE) for Linear Models (LM) are derived to analyze the radiometric values and estimate the uncertainty. The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time. The experiments are carried out under laboratory conditions to minimize the rest of uncertainty sources that might affect the radiometric values. It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

No MeSH data available.


Related in: MedlinePlus

Descriptive statistics (a) and plots (b) for the residuals of the wavelength R (red) for the 12 levels of the reflectance factor.
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f6-sensors-12-10339: Descriptive statistics (a) and plots (b) for the residuals of the wavelength R (red) for the 12 levels of the reflectance factor.

Mentions: The heterogeneity of the residual variance causes the slight non-normality of the distribution for the totality of the residuals, Figure 5. The hypothesis of the significance tests in the ANOVA table is related to the normality of the conditional distribution of the residuals. The distribution of the residuals conditioned to both the reflectance factor r and the spectral response factor S(λ) is normal. Figure 6 shows the descriptive statistics and plots confirming the normality of the conditional distribution for the spectral response factor S(λ) with λ = R (red). The conditional distribution of the spectral response factor S(λ) with λ = G (green) and with λ = B (blue) are also normal.


Grey level and noise evaluation of a Foveon X3 image sensor: a statistical and experimental approach.

Riutort-Mayol G, Marqués-Mateu A, Seguí AE, Lerma JL - Sensors (Basel) (2012)

Descriptive statistics (a) and plots (b) for the residuals of the wavelength R (red) for the 12 levels of the reflectance factor.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472831&req=5

f6-sensors-12-10339: Descriptive statistics (a) and plots (b) for the residuals of the wavelength R (red) for the 12 levels of the reflectance factor.
Mentions: The heterogeneity of the residual variance causes the slight non-normality of the distribution for the totality of the residuals, Figure 5. The hypothesis of the significance tests in the ANOVA table is related to the normality of the conditional distribution of the residuals. The distribution of the residuals conditioned to both the reflectance factor r and the spectral response factor S(λ) is normal. Figure 6 shows the descriptive statistics and plots confirming the normality of the conditional distribution for the spectral response factor S(λ) with λ = R (red). The conditional distribution of the spectral response factor S(λ) with λ = G (green) and with λ = B (blue) are also normal.

Bottom Line: A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper.The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time.It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

View Article: PubMed Central - PubMed

Affiliation: Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Valencia 46022, Spain. gabriuma@upv.es

ABSTRACT
Radiometric values on digital imagery are affected by several sources of uncertainty. A practical, comprehensive and flexible procedure to analyze the radiometric values and the uncertainty effects due to the camera sensor system is described in this paper. The procedure is performed on the grey level output signal using image raw units with digital numbers ranging from 0 to 2(12)-1. The procedure is entirely based on statistical and experimental techniques. Design of Experiments (DoE) for Linear Models (LM) are derived to analyze the radiometric values and estimate the uncertainty. The presented linear model integrates all the individual sensor noise sources in one global component and characterizes the radiometric values and the uncertainty effects according to the influential factors such as the scene reflectance, wavelength range and time. The experiments are carried out under laboratory conditions to minimize the rest of uncertainty sources that might affect the radiometric values. It is confirmed the flexibility of the procedure to model and characterize the radiometric values, as well as to determine the behaviour of two phenomena when dealing with image sensors: the noise of a single image and the stability (trend and noise) of a sequence of images.

No MeSH data available.


Related in: MedlinePlus