.png){kind=icon}
Return
An all-sky camera image classification method using cloud cover features
.png){kind=icon}
OA
AI
DOI：10.5194/amt-15-3629-2022.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 degrees N, 74.53 degrees 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.
Keywords:
FEATURE-EXTRACTION
Journal
IF:
3.3
Papers:
5.3K
Citations:
1.6W
Organization
Cited Papers
ShareSaveEffect of electroacupuncture on discomfort during gastroscopy: a study protocol for a randomized controlled trial.png)
TrialsOAAI
TrialsIF0
OAAIShareSaveShareSaveAn energy balance model exploration of the impacts of interactions between surface albedo, cloud cover and water vapor on polar amplificationCLIMATE DYNAMICS
CLIMATE DYNAMICSIF3.7
PREAI
ShareSave
ShareSaveShareSave
View more