Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

Hsu Yung Cheng, Chih Lung Lin

研究成果: 雜誌貢獻期刊論文同行評審

31 引文 斯高帕斯(Scopus)

摘要

Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

原文???core.languages.en_GB???
頁(從 - 到)199-208
頁數10
期刊Atmospheric Measurement Techniques
10
發行號1
DOIs
出版狀態已出版 - 17 1月 2017

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