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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.

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Related in: MedlinePlus

Temporal evolution of the radiometric values in the sequence of images. Evolution for the spectral response factor S(λ) with λ = R (a). With λ = G (b). With λ = B (c).
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f11-sensors-12-10339: Temporal evolution of the radiometric values in the sequence of images. Evolution for the spectral response factor S(λ) with λ = R (a). With λ = G (b). With λ = B (c).

Mentions: Figure 11 illustrates the temporal trend effect due to the sequential factor t on the radiometric values for each band, i.e., the spectral response factor S(λ). It can be observed a very slight logarithmic trend towards the stabilization of the radiometric values, with different logarithmic forms for each level of the spectral response factor S(λ). Consequently, the temporal trend effect is considered as a covariate in the radiometric response function model (Section 6.1.) through a quantitative factor with a quadratic trend effect in Equation (11).


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)

Temporal evolution of the radiometric values in the sequence of images. Evolution for the spectral response factor S(λ) with λ = R (a). With λ = G (b). With λ = B (c).
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-10339: Temporal evolution of the radiometric values in the sequence of images. Evolution for the spectral response factor S(λ) with λ = R (a). With λ = G (b). With λ = B (c).
Mentions: Figure 11 illustrates the temporal trend effect due to the sequential factor t on the radiometric values for each band, i.e., the spectral response factor S(λ). It can be observed a very slight logarithmic trend towards the stabilization of the radiometric values, with different logarithmic forms for each level of the spectral response factor S(λ). Consequently, the temporal trend effect is considered as a covariate in the radiometric response function model (Section 6.1.) through a quantitative factor with a quadratic trend effect in Equation (11).

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