Cloud Segmentation and Matching Using Deep Learning in All-Sky Images

In this paper, we focus on the segmentation of clouds in All Sky Images using a U-Net-based Deep Learning model and the subsequent recognition of the same cloud in different images. This research lays the foundation for the development of solar radiation forecasts with All-Sky Imagers. The implemented model initially extracts relevant features from the input image using convolutions, thereby reducing the resolution. In the subsequent step, the resolution is restored to its original level using transposed convolutions. Contours are then created from all segmented clouds. Using these contours as references, the same cloud is identified in images from different All-Sky Imagers through template and contour matching. We demonstrate that this segmentation approach yields good results on a small test dataset. Additionally, the recognition of clouds in images from different cameras show promising results, with 75 % of clouds being correctly matched.