Journal
JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
Volume 28, Issue 10, Pages 1286-1296Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JTECH-D-11-00009.1
Keywords
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Funding
- National Natural Science Foundation of China [60805041]
- Chinese Academy of Meteorological Sciences [2007Z001]
- Special Technical D&R Project for Scientific Academies or Institutes of China [NCSTE-2006-JKZX-303]
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Cloud detection is the precondition for deriving other information (e.g., cloud cover) in ground-based sky imager applications. This paper puts forward an effective cloud detection approach, the Hybrid Thresholding Algorithm (HYTA) that fully exploits the benefits of the combination of fixed and adaptive thresholding methods. First, HYTA transforms an input color cloud image into a normalized blue/red channel ratio image that can keep a distinct contrast, even with noise and outliers. Then, HYTA identifies the ratio image as either unimodal or bimodal according to its standard deviation, and the unimodal and bimodal images are handled by fixed and minimum cross entropy (MCE) thresholding algorithms, respectively. The experimental results demonstrate that HYTA shows an accuracy of 88.53%, which is far higher than those of either fixed or MCE thresholding alone. Moreover, HYTA is also verified to outperform other state-of-the-art cloud detection approaches.
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