A new contrast-enhancing feature for cloud detection in ground-based sky images

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

For this study a ground-based sky imaging system was developed that, unlike most other such systems, consists of a low-cost sun-tracking camera fitted with a fish-eye lens. The application of interest is short-term solar power forecasting, so cloud detection is an important step. The hybrid thresholding algorithm proposed by Li et al. for cloud detection is employed. Most cloud detection algorithms make use of the red and blue components in a color image. Though these features perform well for many images, they do not produce good results for the images in this study due to the insufficient contrast between cloud and sky pixels when using ratios between red and blue. To overcome this issue, a new feature, the normalized saturation/value (NSV) ratio, is proposed that is computed in the hue-saturation-value (HSV) color space. This study shows that the NSV ratio produces good contrast between cloud and sky pixels not only for the images in this study but also for general sky images acquired using different camera systems. The reasoning behind the choice of the new ratio is described, and quantitative and qualitative results are presented.

Original languageEnglish (US)
Pages (from-to)209-219
Number of pages11
JournalJournal of Atmospheric and Oceanic Technology
Volume32
Issue number2
DOIs
StatePublished - 2015

Fingerprint

Pixels
Cameras
Color
Imaging systems
Sun
Solar energy
saturation
Lenses
pixel
solar power
Costs
detection
cost

Keywords

  • Bayesian methods
  • Classification
  • Forecasting
  • Forecasting techniques
  • Pattern detection
  • Short-range prediction

ASJC Scopus subject areas

  • Atmospheric Science
  • Ocean Engineering

Cite this

A new contrast-enhancing feature for cloud detection in ground-based sky images. / Jayadevan, Vijai T.; Rodriguez, Jeffrey J; Cronin, Alexander D.

In: Journal of Atmospheric and Oceanic Technology, Vol. 32, No. 2, 2015, p. 209-219.

Research output: Contribution to journalArticle

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