2026-02-09 中国科学院（CAS）
＜関連情報＞
- https://english.cas.cn/newsroom/research_news/phys/202602/t20260210_1150275.shtml
- https://iopscience.iop.org/article/10.1088/1674-4527/ae25c7
ASCNet: ムズタグ・アタ観測地における全天カメラ画像分類に関する研究 ASCNet: Research on All-sky Camera Image Classification at the Muztagh-ata Site
Siqi Wang, Qi Fan, Wenbo Gu, Haozhi Wang, AYZADA Jumahali, Lixian Shen, Daiping Zhang, Liyong Liu and Ali Esamdin
Research Astronomy and Astrophysics Published: 8 January 2026
DOI:10.1088/1674-4527/ae25c7
Abstract
Cloud coverage is one of the crucial elements of site testing in astronomy. All-sky camera (ASC) images are beneficial for our research on cloud coverage. In this paper, we propose ASCNet, an innovative model specifically designed for classifying nighttime ASC images collected at the Muztagh-ata site from 2022 March to 2024 June. ASCNet integrates ResNet34 with an ASCModule, which employs Depthwise Dilated Convolution and embeds lightweight Squeeze-and-Excitation attention within its branches to extract fine-grained texture information from the luminance channel. The data set is partitioned by category, with 70% of images assigned to the training set and 30% to the test set. The model’s performance is assessed by comparing its predictions on the test set with manually annotated labels, yielding a consistency rate of 92.7%. All evaluation metrics of ASCNet are as follows: Accuracy 92.66%, Precision 83.26%, Recall 84.25%, and F1-Score 83.67%, and both ablation and comparative experiments demonstrate significant superiority over other models. A confusion matrix is utilized to analyze the differences between manual classification and model classification. The statistical results demonstrate the model’s excellent classification performance and its robust generalization ability, illustrating that ASCNet has potential for application in future astronomical image classifications.
