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. I NTRODUCTION C LOUD 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 a priori assumptions 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 1545-598X © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.