Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

Abstract

Abstract. Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully convolutional networks (FCNs) to segment cloud pixels from diurnal and nocturnal ASI images; it is called the enhancement fully convolutional network (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red–green–blue (RGB) color space to hue saturation intensity (HSI) color space. Secondly, the I channel of the HSI color space is enhanced by histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100 000 iterative trainings based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with four other algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in cloud segmentation for diurnal and nocturnal ASI images than the other four algorithms.

Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning

“…In other words, the RF model exhibited a tendency to overfit in this study. The accuracy of these results exceeds that of the classification machine learning method (0.60-0.85) presented by Dev et al (2016) using day and night image data, and it is higher than or similar to the accuracy (0.91-0.94) achieved using the regression and deep learning machine learning methods proposed by Shi et al (2019Shi et al ( , 2021 for day and night image data. Apart from the SVR and RF methods, the machine learning methods exhibited similar frequency distributions; however, the accuracy, recall, precision, and R were lower and the RMSE values were higher in the order of GBM, kNN, ANN, and MLR.…”

“…This is because the colors of the sky and clouds vary with the atmospheric conditions and because the sun position and threshold conditions can change continuously (Yabuki et al, 2014;Blazek and Pata, 2015;Cazorla et al, 2015;Calbó et al, 2017). Therefore, methods of cloud detection and cloud cover calculation involving application of machine learning methods to images are now being implemented, as an alternative to empirical methods (Peng et al, 2015;Lothon et al, 2019;Al-lahham et al, 2020;Shi et al, 2021).…”

“…Examples include support vector machines (SVMs), decision trees (DTs), gradient boosting machines (GBMs), and artificial neural networks (ANNs) (Çınar et al, 2020;Shin et al, 2020). Deep learning methods that repeatedly learn data features by sub-sampling image data at each convolution step for gradient descent are also available, such as convolutional neural networks (Dev et al, 2019;Shi et al, 2019;Xie et al, 2020). However, this approach is difficult to utilize for nowcasting because considerable physical resources and time are consumed by the learning and prediction processes (Al Banna et al, 2020;Kim et al, 2021).…”

24-hour cloud cover calculation using ground-based imager with machine learning

“…In other words, the RF model exhibited a tendency to overfit in this study. The accuracy of these results exceeds that of the classification machine learning method (0.6-0.85) presented by Dev et al (2016) using day and night image data, and are higher than or similar to the accuracy (0.91-0.94) achieved using the regression and deep learning machine learning methods proposed by Shi et al (2019Shi et al ( , 2021 for day and night image data. Apart from the SVR and RF methods, the machine learning methods exhibited similar frequency distributions; however, the accuracy, recall, precision, and R were lower and the RMSE were higher in the order of GBM, kNN, ANN, and MLR.…”

“…Examples include support vector machines (SVMs), decision trees (DTs), gradient boosting machines (GBMs), and artificial neural networks (ANNs) (Çınar et al, 2020;Shin et al, 2020). Deep learning methods that repeatedly learn data features by sub-sampling image data at each convolution step for gradient descent are also available, such as convolutional neural networks (Dev et al, 2019;Shi et al, 2019;Xie et al, 2020). However, this approach is difficult to utilize for nowcasting because considerable physical resources and time are consumed by the learning and prediction processes (Al Banna et al, 2020;Kim et al, 2021).…”