An all-sky camera image classification method using cloud cover features

Abstract

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 5000 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 96.58 % and F1_score of 96.24 % by a random forest (RF) classifier, which greatly improves the efficiency of automatic processing of the ASC images.