A New Contrast-Enhancing Feature for Cloud Detection in Ground-Based Sky Images

Vijai T. Jayadevan Department of Electrical and Computer Engineering, The University of Arizona, Tucson, Arizona

Search for other papers by Vijai T. Jayadevan in
Current site
Google Scholar
PubMed
Close
,
Jeffrey J. Rodriguez Department of Electrical and Computer Engineering, The University of Arizona, Tucson, Arizona

Search for other papers by Jeffrey J. Rodriguez in
Current site
Google Scholar
PubMed
Close
, and
Alexander D. Cronin Department of Physics, The University of Arizona, Tucson, Arizona

Search for other papers by Alexander D. Cronin in
Current site
Google Scholar
PubMed
Close
Restricted access

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.

Corresponding author address: Vijai T. Jayadevan, Dept. of Electrical and Computer Engineering, The University of Arizona, 1230 E. Speedway Blvd., Tucson, AZ 85721-0104. E-mail: vijai.tj@gmail.com

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.

Corresponding author address: Vijai T. Jayadevan, Dept. of Electrical and Computer Engineering, The University of Arizona, 1230 E. Speedway Blvd., Tucson, AZ 85721-0104. E-mail: vijai.tj@gmail.com
Save
  • Cazorla, A., Olmo F. J. , and Alados-Arboledas L. , 2008: Development of a sky imager for cloud cover assessment. J. Opt. Soc. Amer., 25A, 2939, doi:10.1364/JOSAA.25.000029.

    • Search Google Scholar
    • Export Citation
  • Chow, C. W., Urquhart B. , Lave M. , Dominguez A. , Kleissl J. , Shields J. , and Washom B. , 2011: Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed. Sol. Energy, 85, 28812893, doi:10.1016/j.solener.2011.08.025.

    • Search Google Scholar
    • Export Citation
  • Ghonima, M. S., Urquhart B. , Chow C. W. , Shields J. E. , Cazorla A. , and Kleissl J. , 2012: A method for cloud detection and opacity classification based on ground based sky imagery. Atmos. Meas. Tech. Discuss., 5, 45354569, doi:10.5194/amtd-5-4535-2012.

    • Search Google Scholar
    • Export Citation
  • Jayadevan, V. T., 2013: Forecasting solar power intermittency using ground-based cloud imaging. M.S. thesis, Dept. of Electrical and Computer Engineering, The University of Arizona, 7 pp.

  • Jayadevan, V. T., Rodriguez J. J. , Lonij V. P. A. , and Cronin A. D. , 2012: Forecasting solar power intermittency using ground-based cloud imaging. World Renewable Energy Forum (WREF) 2012, C. Fellows, Ed., Vol. 3, American Solar Energy Society, 2100–2106.

  • Kohavi, R., and Provost F. , 1998: Glossary of terms. Mach. Learn., 30, 271274.

  • Li, C. H., and Lee C. , 1993: Minimum cross entropy thresholding. Pattern Recognit., 26, 617625, doi:10.1016/0031-3203(93)90115-D.

  • Li, Q., Lu W. , and Yang J. , 2011: A hybrid thresholding algorithm for cloud detection on ground-based color images. J. Atmos. Oceanic Technol., 28, 12861296, doi:10.1175/JTECH-D-11-00009.1.

    • Search Google Scholar
    • Export Citation
  • Long, C. N., Sabburg J. M. , Calbó J. , and Pages D. , 2006: Retrieving cloud characteristics from ground-based daytime color all-sky images. J. Atmos. Oceanic Technol., 23, 633652, doi:10.1175/JTECH1875.1.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E., and Coauthors, 2009: Benchmarking of different approaches to forecast solar irradiance. 24th European Photovoltaic Solar Energy Conference and Exhibition, WIP-Renewable Energies, 4199–4208, doi:10.4229/24thEUPVSEC2009-5BV.2.50.

  • Mantelli Neto, S. L., von Wangenheim A. , Pereira E. B. , and Comunello E. , 2010: The use of Euclidean geometric distance on RGB color space for the classification of sky and cloud patterns. J. Atmos. Oceanic Technol., 27, 15041517, doi:10.1175/2010JTECHA1353.1.

    • Search Google Scholar
    • Export Citation
  • Marquez, R., and Coimbra C. F. M. , 2013: Intra-hour DNI forecasting based on cloud tracking image analysis. Sol. Energy, 91, 327336, doi:10.1016/j.solener.2012.09.018.

    • Search Google Scholar
    • Export Citation
  • Mathiesen, P., and Kleissl J. , 2011: Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States. Sol. Energy, 85, 967977, doi:10.1016/j.solener.2011.02.013.

    • Search Google Scholar
    • Export Citation
  • Pagès, D., Calbó J. , Long C. N. , González J. A. , and Badosa J. , 2002: Comparison of several ground-based cloud detection techniques. Geophysical Research Abstracts, Vol. 4, Abstract EGS02-A-EGS02-A.

  • Perez, R., Kivalov S. , Schlemmer J. , Hemker K. Jr., Renné D. , and Hoff T. E. , 2010: Validation of short and medium term operational solar radiation forecasts in the US. Sol. Energy, 84, 21612172, doi:10.1016/j.solener.2010.08.014.

    • Search Google Scholar
    • Export Citation
  • Pfister, G., McKenzie R. L. , Liley J. B. , Thomas A. , Forgan B. W. , and Long C. N. , 2003: Cloud coverage based on all-sky imaging and its impact on surface solar irradiance. J. Appl. Meteor., 42, 14211434, doi:10.1175/1520-0450(2003)042<1421:CCBOAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Remund, J., Perez R. , and Lorenz E. , 2008: Comparison of solar radiation forecasts for the USA. Proceedings of the 23rd European Photovoltaic Solar Energy Conference, D. Lincot, H. Ossenbrink, and P. Helm, Eds., WIP-Renewable Energies, 3141–3143, doi:10.4229/23rdEUPVSEC2008-4BV.1.45.

  • Shields, J. E., Johnson R. W. , and Koehler T. L. , 1993: Automated whole sky imaging systems for cloud field assessment. Preprints, Fourth Symp. on Global Change Studies, Anaheim, CA, Amer. Meteor. Soc., 228231.

  • Shields, J. E., Karr M. E. , Tooman T. P. , Sowle D. H. , and Moore S. T. , 1998: The whole sky imager—A year of progress. Proceedings of the Eighth Atmospheric Radiation Measurement (ARM) Science Team Meeting, U.S. Dept. of Energy Rep. DOE/ER-0738, 677–685.

  • Smith, A. R., 1978: Color gamut transform pairs. Comput. Graphics, 12, 1219, doi:10.1145/965139.807361.

  • Solomon, S., Qin D. , Manning M. , Chen Z. , Marquis M. , Averyt K. , Tignor M. , and Miller H. L. Jr., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Souza-Echer, M. P., Pereira E. B. , Bins L. S. , and Andrade M. A. R. , 2006: A simple method for the assessment of the cloud cover state in high-latitude regions by a ground-based digital camera. J. Atmos. Oceanic Technol., 23, 437447, doi:10.1175/JTECH1833.1.

    • Search Google Scholar
    • Export Citation
  • Yamashita, M., Yoshimura M. , and Nakashizuka T. , 2004: Cloud cover estimation using multitemporal hemisphere imageries. ISPRS 2004: Proceedings of the XXth ISPRS Congress; Geo-Imagery Bridging Continents, O. Altan, Ed., Vol. XXXV, Part B7, Commission VII, ISPRS, 826–829. [Available online at http://www.isprs.org/proceedings/XXXV/congress/comm7/papers/162.pdf.]

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 463 113 10
PDF Downloads 396 81 11