Articles | Volume 8, issue 11
Atmos. Meas. Tech., 8, 4671–4679, 2015
Atmos. Meas. Tech., 8, 4671–4679, 2015

Research article 05 Nov 2015

Research article | 05 Nov 2015

An automated cloud detection method based on the green channel of total-sky visible images

J. Yang1,2, Q. Min2,3, W. Lu1, W. Yao1, Y. Ma1, J. Du3, T. Lu1, and G. Liu4 J. Yang et al.
  • 1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 2Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 4Smart Grid Operation Research Center, China Electric Power Research Institute, Beijing 100192, China

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.