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Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images

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Clouds are one of the most important meteorological phenomena affecting the Earth radiation balance. The increasing development of whole-sky images enables temporal and spatial high-resolution sky observations and provides the possibility to understand and quantify cloud effects more accurately. In this letter, an attempt has been made to examine the machine learning [multilayer perceptron (MLP) neural networks and support vector machine (SVM)] capabilities for automatic cloud detection in whole-sky images. The approaches have been tested on a significant number of whole-sky images (containing a variety of cloud overages in different seasons and at different daytimes) from Vigna di Valle and Tor Vergata test sites, located near Rome. The pixel values of red, green, and blue bands of the images have been used as inputs of the mentioned models, while the outputs provided classified pixels in terms of cloud coverage or others (cloud-free pixels and sun). For the test data set, the overall accuracies of 95.07%, with a standard deviation of 3.37, and 93.66%, with a standard deviation of 4.45, have been obtained from MLP neural networks and SVM models, respectively. Although the two approaches generally generate similar accuracies, the MLP neural networks gave a better performance in some specific cases where the SVM generates poor accuracy.
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666 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 12, NO. 3, MARCH 2015
Neural Networks and Support Vector Machine
Algorithms for Automatic Cloud Classification
of Whole-Sky Ground-Based Images
Alireza Taravat, Fabio Del Frate, Cristina Cornaro, and Stefania Vergari
Abstract—Clouds are one of the most important meteorological
phenomena affecting the Earth radiation balance. The increasing
development of whole-sky images enables temporal and spatial
high-resolution sky observations and provides the possibility to
understand and quantify cloud effects more accurately. In this
letter, an attempt has been made to examine the machine learn-
ing [multilayer perceptron (MLP) neural networks and support
vector machine (SVM)] capabilities for automatic cloud detec-
tion in whole-sky images. The approaches have been tested on a
significant number of whole-sky images (containing a variety of
cloud overages in different seasons and at different daytimes) from
Vigna di Valle and Tor Vergata test sites, located near Rome. The
pixel values of red, green, and blue bands of the images have been
used as inputs of the mentioned models, while the outputs provided
classified pixels in terms of cloud coverage or others (cloud-free
pixels and sun). For the test data set, the overall accuracies of
95.07%, with a standard deviation of 3.37, and 93.66%, with a
standard deviation of 4.45, have been obtained from MLP neural
networks and SVM models, respectively. Although the two ap-
proaches generally generate similar accuracies, the MLP neural
networks gave a better performance in some specific cases where
the SVM generates poor accuracy.
Index Terms—Automatic classification, cloud classification,
neural networks, support vector machine, whole-sky images.
I. INTRODUCTION
CLOUD coverage or cloud fraction measurements are gen-
erally used for flight planning and aviation. On the other
hand, they have a strong impact on the radiation budget and
on the climate change and variability [1]. More recently, with
the growing interest on renewable energy sources (especially
solar energy), information about cloud coverage earned addi-
tional importance for the electricity production forecast from
photovoltaic and solar power systems [2].
The feedbacks of low clouds (a negative feedback) and
high thin clouds (a positive feedback) on the radiation budget
are well known. Reflection and absorption by cloud particles
Manuscript received June 3, 2014; revised August 14, 2014; accepted
September 6, 2014.
A. Taravat and F. Del Frate are with the Department of Civil Engineering
and Computer Science, University of Rome “Tor Vergata,” 00133 Rome, Italy
(e-mail: art23130@gmail.com).
C. Cornaro is with the Department of Enterprise Engineering, University of
Rome “Tor Vergata,” 00133 Rome, Italy.
S. Vergari is with the Center of Meteorological Experimentation, Italian Air
Force, 00062 Rome, Italy.
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/LGRS.2014.2356616
depend on the volume, shape, and thickness of the clouds [3].
In this context, ground-based imaging devices are commonly
used to support satellite studies. There are several reasons for
using ground-based sensors for cloud recognition: 1) localized
(immediately overhead) cloud presence in a given area cannot
be determined using satellite images with high accuracy, and
2) ground-based imaging sensors are cheaper in comparison
with spaceborne platform images [4].
In the related literature, there are many papers which demon-
strate the increased number of ground-based instruments for
whole-sky image acquisitions [5]. Thus, suitable and adequate
image processing procedures are necessary to fully exploit the
huge amount of data available.
Bush et al. provided a classification method based on the
binary decision trees in order to classify the ground-based
images into five different sky conditions [6]. Singh and Glennen
utilized cooccurrence and autocorrelation matrix for ground-
based cloud recognition [4]. Calbo and Sabburg used Fourier
transformation to classify eight predefined sky conditions [7].
Liu et al. extracted some cloud structure features from infrared
images [8]. Heinle et al. proposed an approach based on textural
features such as energy and entropy as an automated classifica-
tion algorithm for classifying seven different sky conditions [3].
Machine learning approaches such as multilayer perceptron
(MLP) neural networks and support vector machines (SVMs)
have already been demonstrated to provide excellent perfor-
mance in the classification of remotely sensed images. Both
techniques are effective as they build input-output relationships
directly from the data without the need of aprioriassumptions
or specific preprocessing procedures. Another advantage is that,
once the training phase is over, the classification is basically ob-
tained in real time with a strong reduction of the computational
burden.
A combination of neural networks with sky image data has
been recently proposed for direct normal irradiance forecasting
models [9]. However, to our knowledge, a detailed analysis of
different machine learning models for automatic classification
of whole-sky images has not been presented so far in the litera-
ture, whereas machine learning models can be very competitive
in terms of accuracy and speed for image classification. Starting
from these motivations, the purpose of the present paper is to
demonstrate the potential of the machine learning approach
for a fast, robust, accurate, and automated whole-sky image
classification approach. The rest of this letter is organized
in four sections. In the following section, the cloud camera
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TARAVAT et al.: NEURAL NETWORKS AND SVM ALGORITHMS FOR AUTOMATIC CLOUD CLASSIFICATION 667
Fig. 1. The SRF-01 Cloud Cam at the centre of meteorological experimenta-
tion (Re.S.M.A.) of Vigna di Valle.
and the associated image data are introduced. Section III
contains a description of the methodology behind the proposed
approach. The results, discussion, and conclusion follow in
Sections IV and V.
II. CAMERA
Only a few research institutions in several countries have
developed noncommercial sky cameras for their own require-
ments [3], [5], [10]. The automatic sky imaging system which
has been used for this experiment is the SRF-01 Cloud Cam
that is a commercial sky camera produced by EKO (Fig. 1).
The SRF-01 Cloud Cams have been installed at the Centre of
Meteorological Experimentation (Re.S.M.A.) of Vigna di Valle
(4206N, 1212E; 266 m a.s.l.) and at the Solar Energy Test
and Research Laboratory (SETR Lab) of Tor Vergata University
(4151N, 1235E), South-East of Rome [11].
The main device consists of a Canon Power Shot A60
digital camera in a weatherproof housing, with a maximum
resolution of 1600 ×1200 pixels in 30-b color JPEG format.
Additional optics extends the field of view to 180, providing
the possibility to make color pictures of the sky. The images are
rectangular, but the whole sky mapped is circular. The center
of the circle is the zenith, and the horizon is along the border
(more details in Kalisch et al.) [3], [10].
The Cloud Camera has been set to acquire two photographs
every 10 min. The first one is well exposed (aperture =1/500
and shutter =8.0), and the second one is lightly under exposed
(aperture =1/1000 and shutter =8.0), which are the optimum
cloud camera settings achieved by [11]. The algorithm for
cloud detection offered by EKO Co. needs both well-exposed
and under-exposed images for sun identification, but in this
study, only the acquisitions from the first exposure mode have
been used for the machine learning algorithms. The PC clock
has been set on UTC, and the daily time interval of image
acquisition was from 5 A.M.to9P.M. in order to consider the
period from sunrise to sunset.
III. METHODS
The considered data set contains an overall number of 250
images, 200 from the Vigna di Valle (4206N, 1212E) test
site and 50 from the Tor Vergata University (4151N, 1235E)
TAB L E I
CLOUD-TYPE CATEGORIES
TAB L E I I
MIN,MAX,AND AVERAGE VALUES (COMPUTED OVER THE
NUMBER OF THE IMAGES CONSIDERED IN EACH GROUP)
OF THE ACCURACIES FOR DIFFERENT TYPES OF CLOUDS
CLASSIFIED BY MLP AND SVM (IN PERCENT)
test site. These 250 images contain all different types of clouds
under a variety of sky conditions at different selected day
times and seasons. The data set has been categorized into
four groups (Table I) according to the International Cloud
Classification System published in WMO (1987). In order to
avoid systematic misclassifications, we have merged some of
the classes (altostratus and stratus, cirrocumulus, and altocumu-
lus). Additionally, the genera cirrus and cirrostratus have been
merged due to the difficulty in detecting very thin clouds (such
as some kinds of cirrostratus). Despite these generalizations, the
resulting classes represent a suitable partitioning of possible sky
conditions which are especially useful for radiation studies.
As a preprocessing task, the interesting part (which is circular
in shape) of each image in the data set has been extracted, and
then, disruptive factors like camera antenna or trees have been
eliminated from the subset images. The preprocessing phase
makes classification and image interpretation more expedient
and accurate.
After the preprocessing phase, all of the images have been
classified by MLP neural networks and SVM classifiers. Then,
the results have been analyzed and compared with each other
and with the results of the multiband thresholding algorithm
(which is the combined result of AND operations for thresh-
olded red, green, and blue bands of the images), which is a
very popular method in this field offered by EKO Co., and
with the results of the k-nearest neighbor classifier presented
by Heinle et al. [3], which is the latest paper published in the
related literature about automatic cloud classification of whole-
sky images.
668 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 12, NO. 3, MARCH 2015
TABLE III
AVERAGE VALUES OF COMMISSION AND OMISSION ERRORS (IN PERCENT)ACHIEVED BY MLP AND SVM FOR DIFFERENT GROUPS OF DATA
MLPs are a very powerful neural network model for pixel-
level classification [12], [13], which is trained by the error back-
propagation algorithm (the most commonly used model from
feedforward family). In the MLP model, the number of units in
the hidden layer and the training phase settings (the number
of training cycles and the pixel selection for training/testing
the model) represent the fundamental tasks. Normalization,
which is a preliminary phase of neural network classifications,
is performed by linear transformation from the image interval
[0–255] to the neural network interval [1,1] (the normalization
phase ensures that the distance measures respond with equal
weight for each input) [14].
A radial basis function kernel, a very powerful kernel for
pixel-level classification [15], has been selected as the primary
SVM kernel function. In SVM (with radial basis function as the
kernel), the setting of C and γrepresents the fundamental task
in the phase of model designing.
Adjustment of the mentioned parameters (for MLPs and
SVM) affects the capability and sensitivity of the models to fit
the dynamic ranges of the pixel values in the images. The IDL
programming software for the preprocessing phase and the neu-
ral network simulator (SNNS) developed at the University of
Stuttgart, Stuttgart, Germany, have been used in implementing
the classification algorithms. Also, the SVM software (SVM-
Light package; URL: http://svmlight.joachims.org) has been
used for SVM processing.
IV. RESULTS AND DISCUSSION
In the MLP classifier, 5423 pixels (extracted from 15 images)
have been used for training/testing the net. These 15 images
contain all different types of clouds under a variety of sky
conditions at different selected times during the day. The train-
ing sets contain 60% of the data, and the test sets contain the
remaining 40% which do not belong to the training sets. Pixel
selection for the training/test set has been performed randomly
and repeated four times.
Several attempts have been made to properly select the
number of units to be considered in the hidden layers of the
MLP. Architecture 3-7-2 has been finally chosen for its good
performance in terms of classification accuracy, root-mean-
square error (rmse), and training time. Here, 10 000 training
cycles were sufficient to train the network. The inputs of the net
consist of red, green, and blue bands, and the output provides
the pixel classification in terms of cloudy pixel or others (cloud-
free pixels or sun). One MLP has been used in classifying all of
the images.
In the SVM classifier, the images have been rescaled between
(0.0–1.0) for training the model. A range of values was tested
for the two SVM parameters C (1–50) and γ(0.1–10). A
grid search method has been used to examine the various
combinations of C and γ, and the best combination has been
finally chosen based on the model performance in terms of
classification accuracy and rmse [16].
The training/test sets are exactly the same as those which have
been used for the MLP classifier. Pixel selection for the training/
test set has been again done randomly and repeated four times.
Random pixel selection allows us to examine the robustness of
the classification algorithms with respect to the variability
of the training data set [16]. For the validation phase and
accuracy assessment, from each image in the data set, 250
pixels have been selected randomly and then labeled by visual
interpretation.
The accuracy of the whole test data set classified by MLP is
95.07%, with a standard deviation of 3.37, while the accuracy
of the SVM classifier is 93.66%, with a standard deviation of
4.45. The average accuracy achieved by the EKO classification
technique is 50.90%, with a standard deviation of 11.25%.
Visual inspection shows that the machine learning approaches
(MLP and SVM) achieve much better detection results under
a variety of conditions in comparison with the results of the
multiband thresholding algorithm. In Heinle’s work [3], the
average accuracy which has been obtained is about 88%. More-
over, the k-nearest neighbor classifier presented by Heinle et al.
is characterized by slow runtime performance and large mem-
ory requirements [3], whereas the classification by the artificial
neural network approach with a 1000 ×1000 image can be
completed in about a few seconds on a personal computer with
an Intel Pentium dual-core, a speed of 2.2 GHz, and a RAM
memory of 2.00 GB, which is much faster than some existing
methods in the literature. This might have a significant impact
on the reduction of the computational burden when large data
sets need to be processed.
The results of the accuracy assessment applied to different
types of clouds are displayed in Tables II and III.
TARAVAT et al.: NEURAL NETWORKS AND SVM ALGORITHMS FOR AUTOMATIC CLOUD CLASSIFICATION 669
Fig. 2. Classification results of six typical examples. (a) and (b) Clear-sky
example (no clouds and cloudiness below 5%). (c) and (d) Example of the
detection of low or midlevel layer of uniform clouds and dark thick clouds.
(e) Example of the detection of low puffy clouds with clearly defined edges and
high thin clouds, wisplike. (f) Example of the detection of high patched clouds
of small cloudlets, mosaiclike. (First column) Original images. (Second and
third columns) Results of the MLP and SVM classifiers. (Last column) Results
of the classification offered by EKO Co.
The MLP classifier generates a satisfactory accuracy on the
clear-sky, stratus-altostratus-nimbostratus, and stratocumulus-
cumulus groups. The average accuracy of the clear-sky class is
98.65%, with a standard deviation of 0.61. The average com-
mission (percentage of extra pixels in the class) and omission
(percentage of pixels left out of the class) errors of the clear-
sky class are 5.14% and 1.33%, respectively. In the case of the
stratus-altostratus-nimbostratus group, the average accuracies
of 96.62% (standard deviation of 0.71%) with 4.35% and 3.37%
commission and omission errors are obtained by the MLP clas-
sifier. The accuracy of the same groups classified by SVM are
97.92%, with a standard deviation of 0.80% (5.25% and 2.08%
commission and omission error averages), and 95.74%, with
a standard deviation of 1.92% (4.78% and 4.25% commission
and omission error averages).
A significant improvement of 3.60% in accuracy (with a
difference of 0.63% in standard deviation) has been obtained
on the cirrus-cirrostratus-altocumulus-cirrocumulus group seg-
mented by MLP with respect to the same data set segmented
by SVM (the average accuracy is 88.03%, with a standard
deviation of 3.03%).
It is necessary to identify the situations where the proposed
approaches generated poor accuracy and see why the models
failed to work correctly in those cases. The worst accuracy
generated by the MLP classifier is 84.88% with 14.20% com-
mission error, which is obtained for the cirrus-cirrostratus-
altocumulus-cirrocumulus group. This accuracy is 3.96%
higher than the worst accuracy obtained by the SVM classi-
fier which is for cirrus-cirrostratus-altocumulus-cirrocumulus
group (80.92% with 14.60% commission error).
This low performance is caused by the thin and transparent
parts of cirrus clouds which cannot be detected by both algo-
rithms (these parts have been classified as clear sky). Moreover,
the so-called “whitening effect” provides a misclassification of
cloud-free pixels (which are brighter due to forward scattering
by aerosols and haze) near the solar disk, and therefore, such
pixels are classified as thin clouds by the algorithms (see also
[3] and [17]). Fig. 2(b), (c), and (f) illustrates the typical
examples where the models generate less accuracy.
V. C ONCLUSION
In the present study, the capabilities of machine learning
algorithms as automated methods for cloud segmentation in
whole-sky images have been demonstrated. Two classification
algorithms (MLP neural networks and SVM) have been com-
pared using a data set containing 250 images from two different
test sites. The same parameters were used for all of the test
images.
The obtained accuracies showed that the machine learning ap-
proaches (MLP and SVM) achieve better detection results under
a variety of conditions with respect to the results of the thresh-
olding algorithm, which is a popular model for whole-sky im-
age classification. The machine learning models generate lower
accuracies for the cirrus-cirrostratus-altocumulus-cirrocumulus
group (caused by cirrus clouds), and in some other cases, the
accuracy is decreased because of the whitening effect near
the solar disk. The results show that the MLP classifier as an
automated algorithm for cloud classification works better in the
situations where SVM generates poor accuracy.
ACKNOWLEDGMENT
The authors would like to thank the associate editor and the
anonymous reviewers for their constructive comments, Colonel
F. Foti and Captain E. Vuerich from the Centre of Meteorolo-
gical Experimentation of the Italian Air Force for hosting the
cloud camera in their field at Vigna di Valle, the Misure com-
pany for kindly providing the Cloud Camera, and Dr. T. Vitti
from Tecnoel Company for the installation and assistance.
670 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 12, NO. 3, MARCH 2015
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... Many studies show that deep learning methods can adaptively learn the deep features of clouds and have higher detection accuracy than traditional machine learning methods [26][27][28][29][30][31]. Liu et al. introduced a neural network for satellite cloud detection tasks, and conducted experiments on the FY-2C satellite cloud image dataset. ...
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... However, various alternative procedures have been developed for CIE standard sky classification [9], due to the scarcity of sky scanners available to gather sky luminance data at ground meteorological stations. In this task, Supervised Machine Learning (SML) procedures are proposed as effective tools for sky classification, based on accessible meteorological indices [10] such as decision trees (DTs) [11], Support Vector Machines (SVMs) [12], and Artificial Neural Networks (ANNs) [13][14][15]. ...
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