A Cloud height observing system using high resolution whole sky image

  • Abstract
  • Keywords
  • References
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  • Abstract

    Several methods were known to observe the cloud height. Since the meteorological administration mainly has been using human eye method, an automatic cloud height observer is proposed to reduce human errors.The proposed cloud observer system is for the cloud automatic observation system which has been operating at weathers stations in Korea since 1993. The high hand camera is adapted to get high resolution images without a capture board that has the low resolution. And the man-made passive filter for extracting sky reign is replaced by the active filter using image processing. The famous BRISK matching is selected instead of the block mating to get more precise disparity form a pair of stereo image.The Cloud cover announced by the Korean Meteorological Agency is the eye observation result nowadays, even though an intelligent cloud observation system was supplied. The reason is the impermissible error in the observed data of the automatic observation system. The current system is applied a man-made passive filter has to be changed periodically due to the boundary transition, but the proposed system is maintenance-free, since the active filter using edge detection algorithm automatically adjusts itself. The measuring of disparity which is important to analyze spatial information, has improved up to the resolution of observing height is 100m by getting image data directly form the high resolution camera, the old resolution from the capture board. Also, the disparity analyzing method is changed to the BRISK matching algorithm instead of the block mating algorism to get more deliberate disparity analysis since the dimension of the block is too big relatively compare to the cloud in the stereo all sky image. The test results are acceptable range which is human observation value ± 10% since the resolution of observing height is 100m at lower cloud in the sky. The proposed system is conducting the comparison research to compare with the result of reading satellite images and ceilometer.


  • Keywords

    BRISK Matching; Cloud Height; Image Processing; Stereo Vision; Whole Sky Images; SWARD.

  • References

      [1] Allmen, Mark C. &Kegelmeyer, Philip Jr., 1997, The computation of cloud base height from paired whole-sky imaging cameras, Machine Vision and Applications, 9(4), pp160-165.

      [2] Costa-Surós, Montserrat &Calbó, Josep& González, Josep-Abel & Sanchez-Lorenzo, Arturo, 2016, Cloud cover estimation based on ceilometer measurements: a comparison with visual observations, 17th International Conference on Clouds and Precipitation, ICCP2016, Poster.

      [3] Kazantzidis, Andreas &Tzoumanikas, P &Bais, &Alkiviadis& Fotopoulos, Spiros &Economou, George, 2012, Cloud Detection and Classification with the Use of Whole-Sky Ground-Based Images, Atmospheric Research, 113, pp80-88.

      [4] Leutenegger, Stefan, et el, 2011, Binary robust invariant scalable keypoints, International conference on computer vision (ICCV), pp. 2548-2555.

      [5] Moldovan, D., Wada, T., 2004, A calibrated pinhole camera model for single viewpoint omnidirectional imaging systems, IEEE International Conference on Image Processing (ICIP), http://ieeexplore.ieee.org/ document/1421738/.

      [6] Neto, SylvioMantelli, et el, 2010, The Use of Euclidean Geometric Distance on RGB Color Space for the Classification of Sky and Cloud Patterns, J. of Atmospheric and Oceanic Technology, Vol 27, pp1504-1517.

      [7] Sturm, Peter, 2016, Pinhole Camera Model, Computer Vision, Springer, pp 610-613.

      [8] Tao, Tangfei, et el, 2008, A fast block matching algorithm for stereo correspondence, IEEE Conference on Cybernetics and Intelligent Systems, pp 38-41

      [9] Ying, Xianghua, et el, 2014, Imposing Differential Constraints on Radial Distortion Correction, the 12th Asian Conference on Computer Vision (ACCV'14), pp 384-398.

      [10] Ying, Xianghua, et el, 2014, Radial distortion correction from a single image of a planar calibration pattern using convex optimization, IEEE International Conference on Image Processing (ICIP), pp3440-3443.




Article ID: 11315
DOI: 10.14419/ijet.v7i2.12.11315

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