AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1191-2017An RGB channel operation for removal of the difference of atmospheric
scattering and its application on total sky cloud detectionYangJunyangjun@camscma.cnMinQilongqmin@albany.eduLuWeitaoMaYingYaoWenLuTianshuState Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, ChinaAtmospheric Sciences Research Center, State University of New York,
Albany, NY 12203, USAJun Yang (yangjun@camscma.cn) and Qilong Min (qmin@albany.edu)29March2017103119112014July201631August201621February201713March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1191/2017/amt-10-1191-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1191/2017/amt-10-1191-2017.pdf
The inhomogeneous sky background presents a great
challenge for accurate cloud recognition from the total-sky images. A
channel operation was introduced in this study to produce a new composite
channel in which the difference of atmospheric scattering has been removed
and a homogeneous sky background can be obtained. Following this, a new
cloud detection algorithm was proposed that combined the merits of the
differencing and threshold methods, named “differencing and threshold
combination algorithm” (DTCA). Firstly, the channel operation was applied
to transform 3-D RGB image to the new channel, then the circumsolar
saturated pixels and its circularity were used to judge whether the sun is
visible or not in the image. When the sun is obscured, a single threshold
can be used to identify cloud pixels. If the sun is visible in the image,
the true clear-sky background differencing algorithm is adopted to detect
clouds. The qualitative assessment for eight different total-sky images
shows the DTCA algorithm obtained satisfactory cloud identification
effectiveness for thin clouds and in the circumsolar and near-horizon
regions. Quantitative evaluation also shows that the DTCA algorithm achieved the
highest cloud recognition precision for five different types of clouds and
performed well under both visible sun and blocked sun conditions.
Introduction
The distribution of clouds in the troposphere affects the earth's radiation
budget and climate change. Satellite remote sensing provides continuous
monitoring for cloud cover states from outer space, and numerous algorithms
have been developed to detect clouds based on different satellite sensors
(Hagihara et al., 2010; Rüthrich et al., 2013). Ground-based cloud
observation can provide more local cloud information and is an effective
tool to validate the results of satellite-based observations. Human
observations were the main method for estimating sky cloudiness in many
countries for the past 100 years (Deutscher Wetterdienst, 2013), and the
cloud fraction was determined by experienced meteorological observers in
oktas or tenths. The cloud observation results of satellite and ground-based
systems were compared by several researchers (Thorsen et al., 2011; Ma et
al., 2014). A more detailed review has been given about the pros and cons of
different cloud observation platforms in Tapakis and Charalambides (2013).
Hemispherical sky imaging technology offers the possibility for automatic
ground-based cloud observations, and plenty of such devices have been
developed (Shields et al., 1993; Long and DeLuisi, 1998; Calbó and
Sabburg, 2008; Cazorla et al., 2008; Huo and Lu, 2009; Yamashita and
Yoshimura, 2012; Yang et al., 2012; Klebe et al., 2014; Chauvin et al.,
2015). All such imagers can provide three-channel red–green–blue (RGB) total-sky
images at given interval, but colors vary significantly across instruments
because of different sensor characteristics and white balance strategies.
Recorded downwelling radiation at the surface is the combined effect of
molecular absorption, Rayleigh scattering, Mie scattering, and solar direct
radiation, leading to white clouds and a visually blue sky phenomenon in
clear sky conditions. Several cloud detection methods have been applied to
total-sky images using this property. Red (R) and blue (B) are the two most
important channels in traditional cloud detection algorithms, which use a
variety of 2-D red and blue channel operations such as R / B, R–B, and
(B-R) / (B+R). The R / B ratio was first applied to segment thin cloud, opaque
cloud and clear-sky cases using two fixed thresholds for the images captured
by whole-sky imager (WSI; Koehler et al., 1991). Long et al. (2006) adopted
0.6 as a single fixed threshold to identify cloud pixels from the images in
the same R / B space but for whole-sky camera, and then a well-designed clear-sky function and some adjustable parameters were set to improve the
recognition accuracy of clouds for the commercial Total-Sky Imager (TSI).
Unlike the R / B ratio, the R–B difference was recommended by Heinle et al. (2010) for their own instrument, and they considered R-B = 30 as an optimal
fixed threshold. In order to combine the advantages of R / B ratio and R–B
difference, several cloud detection algorithms were developed in the
(B-R) / (B+R) space based on a single fixed threshold, adaptive threshold,
and hybrid thresholds, respectively (Yamashita et al., 2004; Yang et al.,
2009; Li et al., 2011). Contrasting with 2-D red and blue channels, several
cloud detection methods were developed in 3-D RGB space. Sylvio et al. (2010) combined Euclidean geometric distance and a Bayesian statistics
algorithm to identify cloud pixels in the whole RGB space. The multicolor
criterion algorithm was proposed to recognize cloud pixels using multiple
thresholds and obtained better identification accuracy than R–B difference
method (Kazantzidis et al., 2012; Wacker et al., 2015). Yamashita and
Yoshimura (2012) set two indices (sky index and brightness index, which were
based on different RGB channel operations) and defined a threshold curve to
detect clouds. Different from the aforementioned methods, a cloud detection
algorithm was developed for high-latitude regions only using a 1-D
saturation channel, which is obtained by converting the images from the 3-D
RGB space to the 3-D intensity hue saturation space (Martins et al., 2003;
Souza-Echer et al., 2006). Numerous uncertainties exist in the above methods
for cloud detection in the near-horizon and circumsolar regions because of
their similar color and brightness distribution with cloud regions. Long (2010) established a statistical method to correct the overestimated cloud
cover in these regions. The brightness distribution in the sky regions of
the total-sky images is inhomogeneous, which also increases identification
errors, especially for thin clouds in conventional cloud detection methods.
The differencing methods were put forward by several researchers and
proposed subtracting the background information from the original image.
Ghonima et al. (2012) established a clear-sky library by simulating the
pixel red / blue ratio (RBR) for the total-sky images acquired on different
clear days and then proposed a classification algorithm to identify cloud
pixels by comparing the RBR of a cloudy image with the RBR of the clear-sky
library. 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. This method obtained
better identification results than the conventional cloud detection methods,
especially in the circumsolar and near-horizon regions. However, the CSBD
algorithm may misclassify dark clouds as clear-sky regions because of their
low brightness values. Overall, the differencing algorithms are highly
suitable for the cases of visible sun in the total-sky images. For those
sun-obscured total-sky images, traditional threshold algorithms are more
suitable than differencing processing because the latter may introduce
detection errors in the circumsolar region. The threshold between these
cases depends heavily on the image's color information and the nonuniform
background, a huge challenge for the threshold methods.
This paper introduces a new RGB channel operation aiming to remove the
inhomogeneous background in the total-sky images and then proposes a cloud
detection method using this channel operation by combining the threshold and
differencing algorithms. Section 2 describes the total-sky cloud imager (TCI) and
the new channel operation. Section 3 presents the cloud detection algorithm
in detail. The proposed cloud detection method is compared with several
traditional algorithms using a large set of total-sky images in Sect. 4.
Section 5 contains the summary and proposals for future research.
Imaging device and RGB channel operation
The total-sky images appearing in this study were captured by a TCI, which was manufactured by the State Key Laboratory of
Severe Weather at the Chinese Academy of Meteorological Sciences. Like other
hemispherical sky imagers, the core components of TCI are a camera and a
fisheye lens. It can produce three-channel RGB total-sky images at fixed
interval. For the sky imaging, the sun is a huge error source because of its
strong direct radiation in the visible range. To alleviate the effects of
the sun, a lot of hemispherical imagers adopt a solar tracking shielding
member to block the direct solar radiation, such as WSI and TSI, while TCI
adopts an automatic exposure technology, instead of a shadowband, to reduce
the saturated pixels in the circumsolar region. To better preserve the
original radiation information of each band, the industrial camera in the
TCI adopts a linear stretch to convert the 12 bit raw data to 8 bit RGB
image and does not make any white balance processing as it may change the
red and blue channels' brightness values. We carried out field cloud
observations using a TCI instrument in Tibetan Plateau
(88.88∘ E, 29.25∘ N)
during 2012 to 2014, which collected a total-sky image every 5 min in
daytime with an effective diameter about 800 pixels after removal of ground
targets. All of the total-sky images appearing in this paper are chosen from
this field observation and imaging time is expressed in China standard time.
The basic concept of the RAS channel. Panel (a) shows the original
TCI image, (b) is the panchromatic channel image, (c) denotes the B–R
channel, which represents the background image of atmospheric scattering,
(d) is the image of RAS channel, (e) denotes the horizontal brightness
distribution for the red, green, and blue channels, and (f) is the horizontal
brightness distribution for (b), (c) and (d).
For a total-sky image, the forward scattering of aerosols and atmospheric
molecules is dominant for the brightness values in the circumsolar region
under clear sky conditions. As for other regions, the Mie scattering of
hydrometeors is responsible for the brightness values of cloud regions,
while the Rayleigh scattering of atmospheric molecules and the Mie
scattering of aerosols together affect the brightness distribution of the
sky region. The inhomogeneous illumination background in the total-sky image
is mainly caused by the difference in atmospheric scattering angles, and to
a lesser degree by the spectral dependence, particularly under low aerosol
loading. Therefore, for clear-sky pixels, a simple subtraction with a proper
combination of three color channels would remove the inhomogeneous background
due to the difference in scattering angles. However, for cloudy pixels,
cloud particles are larger than aerosols and atmospheric molecules,
resulting in different spectral dependences across three color channels from
clear-sky pixels. Hence, it provides a way to distinguish cloudy pixels from
the clear-sky pixels.
Specifically, the proposed channel operation is designed for the removal of
atmospheric scattering (RAS), which is the first step required to calculate
three important channels: the dark channel, bright channel, and panchromatic
channel. The dark channel refers to the channel of the minimum value of each
pixel in the RGB component (He et al., 2011), while the bright channel
represents the maximum value of each pixel in the RGB component. The
panchromatic channel denotes the channel that is sensitive to all visible
colors. The difference between the bright and dark channels represents the
deviation of the atmospheric scattering of each pixel in the visible range,
which can be considered as the atmospheric background. So, the new channel
operation is defined as Eq. (1):
RAS=Y-(L-D),
where RAS is the new channel after channel operation,
Y is the panchromatic channel, L is the bright
channel, and D is the dark channel. More specifically,
Y=0.299R+0.587G+0.114B (Ford and Roberts, 1998),
L =maxR,G,B, and D =minR,G,B. For most of the TCI images, the bright channel is
equal to the blue channel, and the dark channel can be replaced by the red
channel.
Figure 1 illustrates the basic concept of the proposed channel operation.
Figure 1a shows the original TCI image in clear sky condition, captured on 11
June 2013, and its panchromatic channel image is shown in Fig. 1b. Figure 1c
denotes the B–R channel, which represents the background image of
atmospheric scattering and Fig. 1d shows the ultimate RAS channel. Figure 1e
denotes the brightness distributions of red, green, and blue channels along
a horizontal line (red line in the Fig. 1a). The blue channel has the
highest brightness values for all pixels, while the lowest brightness values
almost always appear in the red channel. Figure 1f represents the horizontal
brightness distributions of panchromatic, B–R, and RAS channels. It is clear
that the horizontal brightness distribution of panchromatic channel varies
consistently with that of B–R due to the clear-sky background brightness
distribution. Hence, the brightness values of clear-sky pixels in the RAS
channel are very low except in the pixels between 300 and 500, which are
affected mainly by the strong forward solar radiation.
An example for removal of atmospheric scattering and comparison of
different channels. Panel (a) shows the original TCI image, (b) is the
panchromatic channel image, (c) represents the background image of
atmospheric scattering, which is equal to B–R channel, (d) shows the image
of R / B, (e) represents the image of green channel, (f) is the image of RAS
channel, (g) denotes the horizontal brightness distribution for (b),
(c), and (f), and (h) is the horizontal brightness distribution for (d),
(e), and (f).
Figure 2 shows an example for the removal of the difference of atmospheric
scattering using the new channel operation. Figure 2a is the original TCI
image with obscured sun, captured on 26 August 2012, and its panchromatic
channel is shown in Fig. 2b. Figure 2c represents the background image of
atmospheric scattering and Fig. 2f denotes the ultimate RAS channel, in
which the sky backgrounds are homogeneous and their brightness values
represent mainly aerosol scattering. Figure 2g shows the brightness
distribution of panchromatic, background, and RAS channels along the red
horizontal line in the Fig. 2a. It is obvious that the brightness values of
clear-sky pixels are lower than the cloudy pixels in the RAS channel. 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. 2d) and green channel (Fig. 2e) in Fig. 2h.
The brightness values of dark clouds from the pixels 350 to 400 are even
lower than the sky brightness values from the pixel 700 to 750 in the green
channel, which means these dark clouds may be misclassified as clear-sky
region using a single threshold for the green channel. Contrarily, the
brightness values of dark clouds are obviously higher than those clear-sky
regions in the RAS channel, which ensures these dark clouds can be
accurately identified. Overall, the RAS channel has a clearly homogeneous
background and the difference between the sky and clouds is significant,
making this scene highly suitable for the following cloud detection.
Cloud detection method
This section describes the total-sky cloud detection algorithm using the
proposed RAS channel, named differencing and threshold combination
algorithm (DTCA), which combines the advantages of the threshold and the
differencing methods. An overview about the proposed DTCA algorithm is
introduced first, and then the details of DTCA are described using several
examples. Finally, the applications of the algorithm to the images after
white balance processing or under low visibility are discussed.
Overview
The purpose of cloud detection is to separate the cloud pixels from the
clear-sky background. Firstly, the TCI image is converted to RAS channel in
order to remove the inhomogeneous sky background. Secondly, the position of
the sun in the TCI image can be calculated using a specific sun positioning
algorithm, and then the image can be combined with brightness information in
the circumsolar region to determine whether the sun is covered by clouds.
When the sun is obscured, a single threshold can be used to identify cloud
pixels but, when the sun is visible in the image, the differencing algorithm
is a better choice to detect clouds. In the DTCA algorithm, we select the
CSBD as our differencing method but use the RAS channel instead of the
original green channel. The distinct steps will be illustrated in detail in
the following subsections.
Cloud detection results using single threshold for the RAS
channels. Column (a) is the original TCI images, (b) denotes the images of
RAS channel, and (c) is the ultimate cloud detection results.
DTCA algorithm
DTCA algorithm consists of RAS channel operation, determining whether the
sun is blocked, and using single threshold or CSBD method to obtain cloud
pixels. In the previous section, we have introduced how to do channel
operation and get RAS channels from the 3-D RGB TCI images. The next
important consideration is determining whether or not the sun is visible.
One solution is to use auxiliary information, such as the results of direct
solar radiation measurements (Alonso et al., 2014; Kazantzidis et al.,
2012), but these measurements are not always available. Another way relies
only on image information; the position of the sun in the TCI image is
always changing but depends only on both the imaging time and the
geographical position of the observer. 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. For the sun-visible conditions, the CSBD algorithm is
recommend to perform cloud detection.
Figure 3 shows the cloud detection results of three TCI images using a
single threshold for their RAS channels. Figure 3a shows the original TCI
images, Fig. 3b denotes the images of RAS channel, and Fig. 3c shows the
ultimate cloud detection results. A suitable threshold is the key of a
successful cloud detection algorithm. An exact threshold should be higher
than the sky background brightness and lower than the cloud brightness. That
means the accurate threshold is depend on local climatic conditions. Since
the sky background is mainly related to the aerosol/molecules scattering
intensity in the RAS channels, and the aerosol concentration above the
Tibetan Plateau is very low in most cases, a fixed threshold of 10 is set to
perform binarization for the RAS channels in our experiments. The first two
examples in Fig. 3 show a good performance for the single threshold algorithm
when the sun is obscured in the total-sky images. When the sun is visible,
the single threshold method unsurprisingly results in detection errors,
especially in the circumsolar region (see the last example in Fig. 3). This
is because strong direct solar radiation causes the pixels in the
circumsolar region to have a similar brightness distribution to the cloud
regions. So the CSBD algorithm is applied to perform cloud detection when
the sun is visible in the TCI images.
Cloud detection result using the CSBD algorithm. Panel (a) is the
image after rotation from the image of the third row of Fig. 3a, (b) shows
the RAS channel image of (a), (c) is the clear-sky image with the same solar
elevation angle as (a), (d) shows the RAS channel image of (c),
(e) represents the new RAS channel of (d) after brightness enhancement for the
circumsolar region, (f) denotes the difference of (b) and (e), (g) shows the
cloud detection result for (a), and (h) is ultimate result after reversing
rotation.
We have built a real clear-sky background library (CSBL) in the previous
CSBD algorithm (Yang et al., 2016). The CSBL includes the initial
creation phase and the subsequent update phase. At the initial stage, the
brightness histogram of each TCI image is analyzed. When the histogram shows
significant unimodal distribution and the peak of the histogram is on the
low brightness side, the image can be considered as clear sky (Yang et al.,
2015). Then the image is rotated by an angle equal to its solar azimuth
angle. The rotated image is one of background images in the CSBL,
which consists of series of real clear-sky images with a solar zenith angle
interval of 1∘. At the update stage, the results of cloud
detection and brightness histogram analysis are combined to determine
whether the image is clear sky. Considering the aerosols and climate
seriously affect the brightness distribution of the clear-sky background,
the CSBL is updated on each clear-sky day to ensure that the clear-sky background image with the closest date as the TCI image is available for
cloud detection. Figure 4 shows an example of cloud detection using CSBD
algorithm. Figure 4a is the image after rotation from the image of the third
row of Fig. 3a, which was captured on 21 June 2013. Figure 4b shows the RAS
channel image of Fig. 4a, then the clear-sky image, which was shot on 11
June 2013 and had the same solar zenith angle as Fig. 4a, is picked out from
the CSBL and shown in Fig. 4c. Figure 4d shows the RAS channel image of Fig. 4c. When the sun is shining on the hemispherical shield of the TCI device,
it produces significant noise in the circumsolar region. To better reduce
the detection errors in the circumsolar region, we enhanced the brightness
values in the circumsolar region by multiplying an empirical coefficient.
Here, we set the factor equal to 2. Figure 4e represents the new RAS channel
of Fig. 4d after brightness enhancement for the circumsolar region, and Fig. 4f denotes the difference of Fig. 4b and e. The background brightness
is very small in the differencing image (Fig. 4f) because of their close
dates (Fig. 4a and c) and low aerosol concentration in the Tibetan
Plateau. Due to the potential difference in aerosol loading in two different
images (days), the clear-sky backgrounds in the reference image and in the
processing image may not be the same. We assume that the difference or the
noise level in the clear-sky background is small. Therefore, we set a
threshold of 10 for the differencing algorithm. Figure 4g shows the result of
binarization processing for Fig. 4f, and Fig. 4h is the ultimate result
obtained by reversing rotation an angel of solar azimuth. Comparing the
result of CSBD with that of threshold method (Fig. 4h and the last row of
Fig. 3c), it can be clearly seen that the CSBD algorithm obtained
satisfactory cloud identification results in the whole image.
Cloud detection as Fig. 3 but for the TCI images after white
balance processing. Column (a) is the TCI images after white balance
processing, (b) denotes the images of RAS channel, and (c) is the ultimate
cloud detection results.
DTCA algorithm for the images after white balance processing
We have reserved the original radiation relationship for each channel in the
TCI images, which use a linear stretch to transfer the raw data to RGB image
without white balance processing. The performance of the DTCA method for
these images has been described in the last subsection. However, most of
hemispherical sky imagers adopt a certain automatic white balance technique
to obtain RGB images, which are more consistent with human vision. To check
whether the DTCA algorithm is applicable to the images after white balance
processing, we do automatic white balance processing (Liu et al., 1995) for
a few TCI images and perform cloud detection for these images using a single
threshold. Figure 5a is the TCI images after white balance processing, Fig. 5b
denotes the images of RAS channel, and Fig. 5c is the ultimate cloud
detection results. For the sun-obscured condition, the sky background in the
RAS channel is homogeneous but with a relatively high brightness value. Thus
in this case, a threshold equal to 20 is set to perform binarization and the
cloud identification result is satisfactory (see the first row of Fig. 5).
When the sun is visible, many errors are still present because of the strong
solar radiation (see the second row of Fig. 5). This implies the single
threshold method is unsuitable for the sun-visible conditions.
Cloud detection as Fig. 4 but for the TCI image after white balance
processing. Panel (a) is the image after rotation from the image of the
second row of Fig. 6a, (b) shows the RAS channel image of (a), (c) is the
clear-sky image with the same solar elevation angle as (a), (d) shows the
RAS channel image of (c), (e) represents the new RAS channel of (d) after
brightness enhancement for the circumsolar region, (f) denotes the
difference of (b) and (e), (g) shows the cloud detection result for (a), and
(h) is ultimate result after reversing rotation.
Figure 6 shows the cloud detection result using CSBD algorithm for the image
of the second row of Fig. 5a, which was captured on 17 October 2012. Figure 6a
is the image after rotation from the image of the second row of Fig. 5a and
its RAS channel is shown in Fig. 6b. Figure 6c denotes the clear-sky image,
which was shot on 8 October 2012 and had the same solar elevation angle as
Fig. 6a. Figure 6d and e show the RAS channel of the clear-sky image and
the new RAS channel after brightness enhancement for the circumsolar region,
respectively. Figure 6f denotes the difference between Fig. 6b and e,
and Fig. 6g shows the cloud detection result for Fig. 6a, which is visually
satisfactory. The ultimate result after reversing rotation is shown in Fig. 6h. The experimental results explain that the DTCA algorithm is still
effective for the images with automatic white balance processing.
Cloud detection as Fig. 3 but for the TCI images under low
visibility. Column (a) is the original TCI images, (b) denotes the images of
RAS channel, and (c) is the ultimate cloud detection results.
DTCA algorithm for the images under low visibility
Low visibility is caused mainly by fog and haze, which not only seriously
affect the image quality but also impose difficulties for accurate cloud
identification. Mie scattering of aerosol particles is responsible for the
most of visibility reduction, which has similar scattering intensity in the
visible range and makes the sky background appear as grayish white color. Fig. 7a shows two images under low visibility, both of which were acquired on 24
November 2012. It is clear that the sky backgrounds have very high
brightness values in their RAS channels (Fig. 7b). The adaptive thresholds,
which are relative to the concentration of aerosol, should be considered for
binarization of RAS channels. The results of cloud detection are shown in
Fig. 7c. The Mie scattering and forward scattering of aerosols result in
evident cloud identification errors in the circumsolar region.
Similarly, the CSBD algorithm is applied to detect clouds when the sun is
visible (the second row of Fig. 7a). The key point of the CSBD algorithm is
that the clear-sky image should have a background similar to that in the
cloudy image. Fortunately, the clear-sky image (Fig. 8c) was captured also
on 24 November 2012, and its concentration of aerosol was very similar with
that in Fig. 8a. Figure 8 represents the detailed cloud detection steps
based on the CSBD algorithm. The identification result (Fig. 8h) has
significant improvement compared with the result of threshold algorithm, but
it still misses many cloud pixels because the brightness values of these
pixels are lower than the brightness values of the sky region. The take-away
is that as long as the brightness values of cloud pixels are higher than
those of the sky region under low visibility, those cloud pixels can be
identified successfully. Contrarily, when the concentration of aerosol is
high enough to shelter the cloud regions, it is impossible to accurately
identify clouds using a single visible imager.
Cloud detection as Fig. 4 but for the TCI image under low
visibility. Panel (a) is the image after rotation from the image of the
fourth row of Fig. 7a, (b) shows the RAS channel image of (a), (c) is the
clear-sky image with the same solar elevation angle as (a), (d) shows the
RAS channel image of (c), (e) represents the new RAS channel of (d) after
brightness enhancement for the circumsolar region, (f) denotes the
difference of (b) and (e), (g) shows the cloud detection result for (a), and
(h) is ultimate result after reversing rotation.
Comparison of different cloud detection methods. Column (a) is the
original TCI images, (b) shows the results of R / B, (c) represents the
results of multicolor method, (d) shows the results of GBSAT, (e) denotes
the results of CSBD, and (f) shows the results of the proposed DTCA method.
The recognition error rates of different cloud detection algorithms
in percentage.
The recognition error rates under different sun conditions.
Visible sun Blocked sun AvgSDAvgSDR / B-8.813.6-15.213.9Multicolor31.825.2-24.332.0CSBD0.514.9-15.525.2DTCA-2.210.2-5.910.1Results comparison
To better explain the performance of the proposed DTCA algorithm, its
identification results for eight different TCI images were compared with
several traditional cloud detection methods, including R / B, multicolor,
GBSAT, and CSBD. These traditional algorithms have both threshold methods
(R / B and multicolor) and differencing methods (GBSAT and CSBD). The channels
used in these traditional algorithms include 2-D red and blue channels, 3-D
RGB channels, and a 1-D green channel. Figure 9 shows the cloud
identification results of different algorithms, in which the black regions
denote sky and the white regions denote cloud pixels. Figure 9a represents the
original TCI images, the results of R / B are shown in Fig. 9b, Fig. 9c
represents the results of multicolor, Fig. 9d shows the results of GBSAT,
the results of CSBD are shown in Fig. 9e, and Fig. 9f denotes the results of
the proposed DTCA method. When the sun is invisible in the TCI images, the
R / B algorithm has very good identification precision for the opaque clouds
but has poor precision for thin clouds. However, when the sun is visible in
the TCI images, the R / B algorithm has obvious detection errors in the
circumsolar region. For the multicolor algorithm, the recognition precision
is low for almost all the TCI images. The reason may be that the several
fixed thresholds are not adequate for our TCI sensors and the local
atmospheric conditions. The GBSAT algorithm has obvious improvement in the
circumsolar region, but the simulated background sometimes over- or
underestimates the background brightness values, leading to the introduction
of detection errors. The CSBD algorithm can identify cloud pixels accurately
when the sun is visible, but it is inappropriate for the sun-obscured
conditions. The identification errors for dark clouds in the GBSAT and CSBD
methods were caused mainly by the green channel, in which the brightness
values of some dark clouds are lower than those of sky background. Overall,
the threshold algorithms are good for thick clouds, while the differencing
algorithms obtain better identification for thin clouds and have higher
detection accuracy in the circumsolar and near-horizon regions. The proposed
DTCA algorithm combined the merits of the two kinds of algorithms and
acquired more satisfactory results for all these cases.
In addition to qualitative assessment, the quantitative evaluation can
provide a more objective comparison. We have established a total-sky image
set (available after signing the user license agreement at http://www.camscma.cn/cgi/agreement-e.pdf), in which the images
are divided into five sky types: clear sky, cirriform, stratiform,
cumuliform, and mixed clouds. Each type contains 1000 images captured in
Tibetan Plateau during 2012 and 2014. We randomly selected 50 total-sky
images from each type for quantitative evaluation of cloud detection
algorithms. The cloudiness results were given by two experienced weather
observers in percentage, and then their results were averaged for each test
image as standard cloudiness. Since the GBSAT and CSBD algorithms are aimed
at partly cloudy images, which are not suitable for the clear-sky and
overcast images, we only compared the results of R / B, multicolor, and DTCA.
The identified cloudiness of each method was compared with the human
standard cloudiness, image by image. The average recognition error rates and
their standard deviations for different cloud detection algorithms are shown
in Table 1. Here, negative values denote underestimation, and positive
values mean overestimation. The conclusions are similar to the qualitative
assessment in that the multicolor algorithm is poor for all types of TCI
images, the identification precision is low for the cirriforms in the R / B
algorithm. The average recognition error rate of DTCA algorithm is -5.2 %,
but the error rate is -19.6 for the cirriforms, which means it still
underestimates some thin clouds.
To better emphasize the merit of the proposed algorithm, we also compared
the error rates under different sun conditions. We randomly selected 100
total-sky images (50 visible sun cases and 50 blocked sun cases) from the
mixed cloud type for quantitative evaluation of cloud detection algorithms.
The results are shown in Table 2. The CSBD algorithm performs well under
visible sun conditions but poorly under fully blocked sun conditions. The
DTCA algorithm obtains the best recognition accuracy under both conditions.
Conclusions
A big challenge for accurate cloud detection algorithms is the inhomogeneous
brightness distribution of sky background. The solutions for the existing
methods are mainly based on differencing technology, which use the original
image to subtract the simulated or true clear-sky background image. This
paper proposed a RAS channel using a simple RGB channel operation. The RAS
channel can effectively remove the difference of atmospheric scattering in
the total-sky images, especially when atmospheric aerosol concentration is
low. Then the DTCA algorithm was proposed to detect clouds, which combined
the merits of the threshold and the differencing methods. The RAS channel
was first calculated in the DTCA algorithm, and then sun visibility was
determined by using the circularity of the circumsolar saturated pixels.
When the sun is blocked, the single threshold strategy was adopted to
identify cloud pixels, while the CSBD algorithm was used for cloud detection
in the sun-visible cases. The experimental results for eight different total-sky images showed that the DTCA algorithm was much more effective at cloud
identification than several traditional algorithms. The quantitative
evaluation also stated clearly that the DTCA algorithm has the best
identification results for all types of clouds and under both visible sun
and blocked sun conditions.
Considering that the total-sky images acquired by most of the hemisphere sky
imagers are processed by automatic white balancing, we also tested detection
effectiveness of the DTCA algorithm for those processed images. The
experimental results show that the DTCA algorithm is still applicable to the
total-sky images after automatic white balance processing. As low visibility
is still a big obstacle for accurate cloud recognition, we performed a
preliminary experiment to test the applicability of DTCA algorithm to images
under low visibility. The DTCA algorithm identified a portion of the cloud
pixels successfully, but many cloud pixels were missed because their
brightness values were lower than the sky background. In this situation,
when the concentration of aerosol is high enough to shelter the cloud
regions, it is impossible to identify clouds only using visible imager. Some
microwave sensors that can penetrate aerosols should be considered for cloud
recognition under such low visibility.
Data used in this study can be made available upon request to the author.
The authors declare that they have no conflict of interest.
Acknowledgements
We gratefully acknowledge the support from the National Natural Science
Foundation of China (41675030 and 41105121), the grant financed by the
National Key Scientific Instrument and Equipment Development Projects of
China (2012YQ11020504), and the Basic Research Fund of Chinese Academy of
Meteorological Sciences.
Edited by: A. Lambert
Reviewed by: two anonymous referees
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