Sky cloud images captured by ground-based cameras (a.k.a. whole sky imagers) are increasingly used nowadays because of their applications in a number of fields, including climate modeling, weather prediction, renewable energy generation, and satellite communications. Due to the wide variety of cloud types and lighting conditions in such images, accurate and robust segmentation of clouds is challenging. In this paper, we present a supervised segmentation framework for ground-based sky cloud images based on a systematic analysis of different color spaces and components, using partial least squares (PLS) regression. Unlike other state-of-the-art methods, our proposed approach is entirely learning-based and does not require any manually-defined parameters. In addition, we release the Singapore Whole Sky IMaging SEGmentation Database (SWIMSEG), a large database of annotated sky cloud images, to the research community.
 Abstract This work describes the development of a simple method of field estimating the sky cloud coverage percentage for several applications at the Brazilian Antarctic Station, Ferraz (62°05′S, 58°23.5′W). The database of this method was acquired by a digital color camera in the visible range of the spectrum. A new algorithm was developed to classify each pixel according to a criteria decision process. The information on the pixel contamination by clouds was obtained from the saturation component of the intensity, hue, and saturation space (IHS). For simplicity, the images were acquired with a limited field of view of 36° pointing to the camera’s zenith to prevent direct sunlight from reaching the internal charge-coupled device (CCD) on the camera. For a priori–classified clear-sky images, the accuracy of the method was superior to 94 . For overcast-sky conditions, the corresponding accuracy was larger than 99 . A comparison test was performed with two human observers and our method. The results for the... A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers.  AbstractCloud detection is the precondition for deriving other information (e.g., cloud cover) in ground-based sky imager applications. This paper puts forward an effective cloud detection approach, the Hybrid Thresholding Algorithm (HYTA) that fully exploits the benefits of the combination of fixed and adaptive thresholding methods. First, HYTA transforms an input color cloud image into a normalized blue red channel ratio image that can keep a distinct contrast, even with noise and outliers. Then, HYTA identifies the ratio image as either unimodal or bimodal according to its standard deviation, and the unimodal and bimodal images are handled by fixed and minimum cross entropy (MCE) thresholding algorithms, respectively. The experimental results demonstrate that HYTA shows an accuracy of 88.53 , which is far higher than those of either fixed or MCE thresholding alone. Moreover, HYTA is also verified to outperform other state-of-the-art cloud detection approaches. Cloud detection plays an essential role in meteorological research and has received considerable attention in recent years. However, this issue is particularly challenging due to the diverse characteristics of clouds. In this letter, a novel algorithm based on superpixel segmentation (SPS) is proposed for cloud detection. In our proposed strategy, a series of superpixels could be obtained adaptively by SPS algorithm according to the characteristics of clouds. We first calculate a local threshold for each superpixel and then determine a threshold matrix for the whole image. Finally, cloud can be detected by comparing with the obtained threshold matrix. Experimental results show that our proposed algorithm achieves better performance than the current cloud detection algorithms. Automatic cloud extraction from satellite imagery is an important task for many applications in remote sensing. Humans can easily identify various clouds from satellite images based on the visual features of cloud. In this study, a method of automatic cloud detection is proposed based on object classification of image features. An image is first segmented into superpixels so that the descriptor of each superpixel can be computed to form a feature vector for classification. The support vector machine algorithm is then applied to discriminate cloud and noncloud regions. Thereafter, the GrabCut algorithm is used to extract more accurate cloud regions. The key of the method is to deal with the highly varying patterns of clouds. The bag-of-words (BOW) model is used to construct the compact feature vectors from densely extracted local features, such as dense scale-invariant feature transform (SIFT). The algorithm is tested using 101 RapidEye and 86 Landsat images with many cloud patterns. These images achieve 89.2 of precision, 87.8 of recall for RapidEye, 85.8 of precision, and 83.9 of recall for Landsat. The experiments show that the method is insensitive to the number of codewords in the codebook construction of the BOW.