![]() ![]() Additionally, research involving natural illumination generally requires knowledge about clouds and their position with respect to the sun’s disk (e.g., Chen et al. Most cloud-related studies require some sort of cloud observations, such as the amount and type of clouds that are present. In addition, clouds affect our everyday lives, for example, by modifying the amount of ultraviolet (UV) radiation that reaches the earth’s surface ( Calbó et al. It is broadly recognized, for example, by the Intergovernmental Panel on Climate Change (IPCC), that clouds (and cloud–aerosol interaction) are responsible for the largest uncertainties in climate models and climate predictions ( Houghton et al. Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques.Ĭlouds are a major meteorological phenomena related to the hydrological cycle and affect the energy balance on both local and global scales through interaction with solar and terrestrial radiation. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia and Girona, Spain, respectively). The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. ![]()
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