Models of sky radiance distribution and sky luminance distribution

Igawa, Norio; Koga, Yasuko; Matsuzawa, Tomoko; Nakamura, Hiroshi

doi:10.1016/j.solener.2004.04.016

Cited by 168 publications

(82 citation statements)

References 17 publications

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“…T eff accounts for all radiance losses at the image sensor due to the camera optics, including illumination factors, external and internal reflections, and absorption within the lens. As expected from [6][7][8][9][10][11][12][13], Fig. 4's largest L occur near the zenith.…”

Section: A All-sky Maps Of Overcast Radiancesupporting

confidence: 71%

“…Central to many such models is the assumption that, if shortterm brightness fluctuations [4,5] are excluded, either luminance L v or visible-wavelength radiance L is largest at and symmetric about the zenith in optically thick overcasts [6][7][8][9]. Indeed, several measurement campaigns have produced data that apparently support this assumption [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Observed brightness distributions in overcast skies

Lee

Devan

2008

Appl. Opt.

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Beneath most overcasts, clouds' motions and rapidly changing optical depths complicate mapping their angular distributions of luminance L v and visible-wavelength radiance L. Fisheye images of overcast skies taken with a radiometer-calibrated digital camera provide a useful new approach to solving this problem. Maps calculated from time-averaged images of individual overcasts not only show their brightness distributions in unprecedented detail, but they also help solve a long-standing puzzle about where brightness maxima of overcasts are actually located. When combined with simulated radiance distributions from MODTRAN4, our measured radiances also let us estimate the gradients of cloud thickness observed in some overcasts.

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Section: A All-sky Maps Of Overcast Radiancesupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Observed brightness distributions in overcast skies

Lee

Devan

2008

Appl. Opt.

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“…The technique described here is analogous to empirical relationships established to derive atmospheric properties such as turbidity and clear sky index from relative total irradiance [23]. As an easier alternative, measurements may be discretized into a "quasi clear sky set" containing scans with no obscuration and an "obscured sky set" containing scans with full obscuration (high values of COD), turning g into a constant spectrum.…”

Section: Normalization Factor For Dual-beam Brdf Effectsmentioning

confidence: 99%

Evaluation and Normalization of Cloud Obscuration Related BRDF Effects in Field Spectroscopy

et al. 2009

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The impact of target bidirectional reflectance in dual field of view spectroscopy was described and quantified using field measurements and ray-tracing simulations. A data-driven normalization method was developed to convert reflectance factors under cloud obscured conditions into clear sky reflectance by decomposing the target bidirectional reflectance into an isotropic target-specific component and a group-specific bidirectional component. An evaluation on tree, grass and gravel targets suggests a reduction in relative reflectance error obtained by normalization from 15% to less than 5% between 400 and 1800 nm. At higher wavelengths a decreased signal-to-noise ratio increases the errors.

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“…In the literature, it is common to find physically-based models that express the sky luminance as a function of diverse variables, among them, sun position and atmospheric turbulence [9,15]. These models have been used in Computer Vision under some constraints, like a fixed point of view.…”

Section: Sky Model In the Lamentioning

confidence: 99%

Finding Images with Similar Lighting Conditions in Large Photo Collections

Díaz

Sturm

2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. When we look at images taken from outdoor scenes, much of the information perceived is due to the ligthing conditions. In these scenes, the solar beams interact with the atmosphere and create a global illumination that determines the way we perceive objets in the world. Lately, exploration of the sky like the main illuminance component has began to be explored in Computer Vision. Some of these studies could be classified like color-based algorithms while some others fall in the physicsbased category. However most of them assume that the photometric and geometric camera parameters are constant, or at least, that they could be determined. This work presents a simple and effective method in order to find images with similar lighting conditions. This method is based on a Gaussian mixture model of sky pixels represented by a 3D histogram in the La * b * color space.

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Models of sky radiance distribution and sky luminance distribution

Cited by 168 publications

References 17 publications

Observed brightness distributions in overcast skies

Observed brightness distributions in overcast skies

Evaluation and Normalization of Cloud Obscuration Related BRDF Effects in Field Spectroscopy

Finding Images with Similar Lighting Conditions in Large Photo Collections

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