Vol.20, NO.6, 2009
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Article Navigation >  Journal of Applied Meteorological Science  > 2009  >  20(6): 713-721
Yang Jun, Lǜ Weitao, Ma Ying, et al. An automatic ground-based cloud detection method based on adaptive threshold. J Appl Meteor Sci, 2009, 20(6): 713-721.
Citation: Yang Jun, Lǜ Weitao, Ma Ying, et al. An automatic ground-based cloud detection method based on adaptive threshold. J Appl Meteor Sci, 2009, 20(6): 713-721.
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An Automatic Ground-based Cloud Detection Method Based on Adaptive Threshold

  • 1.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2.

    School of Computer and In formation Technology, Beijing Jiaotong University, Beijing 100044

  • Received Date: 2008-10-14
  • Rev Recd Date: 2016-01-13
  • Publish Date: 2009-12-31
    Abstract
  • Clouds affect the energy balance of the earth by means of absorbing and scattering radiation, and they have notable influences on global climate. It is very important to monitor clouds and there are several meteorological satellites providing sky-based large-scale scope clouds observations round-the-clock. However, the ground-based cloud observations mainly depend on visual judgments of the meteorological observers, which have become a bottleneck of automatic meteorological observation.Computing cloudage automatically is very important, and cloud detection is the basis of cloudage computation.Up to now, the mature ground-based cloud detection methods are still based on thresholds.For instance, the threshold 1.3 is recommended for ratio of ground-based blue band and red band observations. However, considering the complexity of clouds, a fixed threshold obviously cannot obtain satisfactory detection effect for different types of clouds. In clear sky, clouds are white against the blue background, so an automatic ground-based clouds detection method can be established in terms of the maximum interclass variance adaptive threshold selection. Ratio, difference, normalized difference of blue band and red band are calculated respectively, then bimodal distributing can be found in the image gray histogram, and then an adaptive threshold can be obtained using the maximum interclass variance method. Compare the band operation result with the adaptive threshold pixel by pixel, a pixel whose gray value is less than the adaptive threshold can be regarded as cloud pixel, else non-cloud pixel.Using this rule, the cloud regions can be separated from the sky background.Three different types of ground-based clouds are analyzed, and the proposed adaptive threshold method performs more appropriately comparing with fixed threshold cloud detection method.The blue band and red band ratio method can detect the maximum cloudage, and the difference operation method can detect the minimum cloudage. But quantitative assessment results show that, the ratio method mistakes many non-cloud pixels into clouds, while the norm alized difference processing is satisfactory both in correctness and accuracy. It should be pointed out that the proposed method applies only to clear sky, as for other weather conditions, more researches are needed.
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    References(17)

  • Fig. 1  Ground-based cloud detection based on fixed threshold

    (a)cloud image 1, (b)result of cloud image 1, (c)cloud image 2, (d)result of cloud image 2

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    Fig. 2  The flow chart of ground-based cloud automatic detection

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    Fig. 3  Adaptive threshold detection for cloud image 1

    (a)ratio operation, (b)adaptive threshold detection result of ratio, (c)difference operation, (d)adaptive threshold detection result of difference, (e)normalized difference operation, (f)adaptive threshold detection result of normalized difference

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    Fig. 4  Histograms after blue and red band processing

    (a)ratio operation, (b)difference operation, (c)normalized difference operation

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    Fig. 5  Adaptive threshold detection for cloud image 2

    (a)cloud image 2, (b)detection result of ratio, (c)detection result of difference, (d)detection result of normalized difference

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    Fig. 6  A daptive threshold de tection for cloud image 3

    (a)cloud image 3, (b)detection result of ratio, (c)detection result of difference, (d)detection result of normalized difference

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    Fig. 7  Cloud masks of manua l interpretation

    (a)mask of cloud image 1, (b)mask of cloud image 2, (c)mask of cloud image 3

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    Table  1  Precision assessment for cloud detection
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    • Received : 2008-10-14
    • Accepted : 2016-01-13
    • Published : 2009-12-31
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