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

Abstract

Abstract.Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-toblue 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.

An RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

“…The lower the aerosol concentration in the sky, the more the sky brightness values tend toward zero. We had compared the brightness distribution between R / B, (B−R) / (B+R), and green channels in our previous study (Yang et al, 2015) and showed the green channel is a better choice for cloud detection, but dark clouds may be misclassified as clear-sky and the sky background in the green channel is still inhomogeneous. To better describe the merit of RAS channel, we compared horizontal brightness distribution of the RAS channel with R / B channel ( Fig.…”

“…Calculating the solar position requires two basic steps: one is to compute the solar zenith angle and azimuth and the other is to determine the specific coordinates of the sun in the image. The same steps as mentioned in Yang et al (2015) are adopted in this study to accurately calculate the central coordinate of the sun in the TCI image, and then the circumsolar saturated pixels and their circularity can be used to determine whether the sun is visible (Yang et al, 2015). When the sun is blocked, the single threshold algorithm is applied to identify clouds.…”

“…clear sky (Yang et al, 2015). Then the image is rotated by an angle equal to its solar azimuth angle.…”

“…A least-square fitting algorithm was developed to simulate clear-sky background in the normalized R / B ratio (NRBR) space and the background was subtracted from the NRBR of the cloudy image to get cloud pixels (Chauvin et al, 2015). Yang et al (2015) simulated the image background information using a morphological open operation and proposed the background subtraction adaptive threshold algorithm based on a 1-D green (G) channel (GBSAT) to identify cloud pixels from the cloudy images. Simulated background is not always a good representation of the real background information; Yang et al (2016) proposed a real clear-sky background differencing (CSBD) algorithm using the green channel.…”

From pixels to patches: a cloud classification method based on a bag of micro-structures

“…In addition, a number of other ground-based sky imagers have also been developed in other countries and institutes, such as the whole-sky camera (Calbo and Sabburg, 2008;Heinle et al, 2010) and the totalsky cloud imager (Li et al, 2011;Yang et al, 2012Yang et al, , 2015. Most of these sky imagers capture sky conditions with redgreen-blue (RGB) color images named all-sky images, and are successfully applied to estimate cloud cover.…”