Abstract
The all-sky camera (ASC) images can reflect the local cloud cover information, and the cloud cover is one of the first factors considered for astronomical observatory site selection. Therefore, the realization of automatic classification of the ASC images plays an important role in astronomical observatory site selection. In this paper, three cloud cover features are proposed for the TMT (Thirty Meter Telescope) classification criteria, namely cloud weight, cloud area ratio and cloud dispersion. After the features are quantified, four classifiers are used to recognize the classes of the images. Four classes of ASC images are identified: “Clear”, “Inner”, “Outer” and “Covered”. The proposed method is evaluated on a large dataset, which contains 7328 ASC images taken by an all-sky camera located in Xinjiang (38.19° N, 74.53° E). In the end, the method achieves an accuracy of 97.28 % and F1_score of 96.97 % by a random forest (RF) classifier, which greatly improves the efficiency of automatic processing of the ASC images.
Highlights
Clouds are visible polymers composed of water vapor in the atmosphere liquefied by cold, and they are important for the hydrological cycle and energy balance of the Earth (Sodergren et al, 2017)
4 Results and discussion we evaluate the effectiveness of cloud cover features for the classification of all-sky camera (ASC) images
It can be seen that the average accuracy of each classifier is more than 95%, indicating that the cloud weight, cloud area ratio and cloud dispersion proposed are effective cloud cover 285 features that can classify ASC images based on the Thirty Meter Telescope (TMT) classification criteria, which greatly promotes the automatic processing of the images
Summary
Clouds are visible polymers composed of water vapor in the atmosphere liquefied by cold, and they are important for the hydrological cycle and energy balance of the Earth (Sodergren et al, 2017). Heinle et al (2010) proposed a classification algorithm based on spectral features in RGB color space and texture features extracted by grey-level co-occurrence matrices (GLCMs). This method has a high accuracy for the classification of seven different classes of clouds. The algorithms proposed above, both traditional and deep learning methods, are based on texture, color and spectral features of cloud for classification, and they are all for cloud shape classification of local images or all-sky images. We propose three cloud cover features in this paper that introduces cloud thickness and distribution position into the cloud cover calculation, and classify the ASC images according to the TMT classification criteria.
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