• Buch, K. A., , Sun C.-H. , , and Thorne L. R. , 1995: Cloud classification using whole-sky imager data. Proc. Fifth Atmospheric Radiation Measurement (ARM) Science Team Meeting, San Diego, CA, U.S. Department of Energy, 3539. [Available online at https://www.arm.gov/publications/proceedings/conf05/extended_abs/buch_ka.pdf.]

  • Calbó, J., , and Sabburg J. , 2008: Feature extraction from whole-sky ground-based images for cloud-type recognition. J. Atmos. Oceanic Technol., 25, 314, doi:10.1175/2007JTECHA959.1.

    • Search Google Scholar
    • Export Citation
  • Chen, T., , Li Z. Y. , , Chen Z. Y. , , Lv P. J. , , Zhao B. , , and Lin S. P. , 2009: Application of Hilbert–Huang transform in analysis of earth tide deformation (in Chinese). J. Geod. Geodyn., 29, 131134.

    • Search Google Scholar
    • Export Citation
  • Chen, X.-Y., , Song A.-G. , , Yuan W. , , Zhen J.-J. , , and Li J.-Q. , 2010: On the application of BEMD and Tamura textural feature for recognizing ground-based cloud. ICCASM 2010: The 2010 International Conference on Computer Application and System Modeling, Vol. 12, IEEE, V12-61–V12-64, doi:10.1109/ICCASM.2010.5622164.

  • Gao, F. J., 2008: Bidimensional empirical mode decomposition method and its application research in image processing (in Chinese). M.S. thesis, College of Electrical and Electronic Engineering, Harbin University of Science and Technology, 61 pp.

  • Garrett, T., , and Zhao C. , 2012: Ground-based remote sensing of thin clouds in the Arctic. Atmos. Meas. Tech. Discuss.,5, 86538699, doi:10.5194/amtd-5-8653-2012.

    • Search Google Scholar
    • Export Citation
  • Heinle, A., , Macke A. , , and Srivastav A. , 2010: Automatic cloud classification of whole sky images. Atmos. Meas. Tech., 3, 557567, doi:10.5194/amt-3-557-2010.

    • Search Google Scholar
    • Export Citation
  • Heintzenberg, J., , and Charlson R. J. , 2009: Introduction. Clouds in the Perturbed Climate System, J. Heintzenberg, and R. J. Charlson, Eds., MIT Press, 1–15.

  • Hu, J. N., 2008: Research of texture analysis based on empirical mode decomposition (in Chinese). M.S. thesis, College of Electrical and Electronic Engineering, Changsha University of Science and Technology, 52 pp.

  • Huang, N. E., and Coauthors, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London,454, 903–995, doi:10.1098/rspa.1998.0193.

  • Liu, L., , Sun X. J. , , Chen F. , , Zhao S. J. , , and Gao T. C. , 2011: Cloud classification based on structure features of infrared images. J. Atmos. Oceanic Technol., 28, 410417, doi:10.1175/2010JTECHA1385.1.

    • Search Google Scholar
    • Export Citation
  • Long, P. F., , He L. , , Lv H. , , and Zhang C. , 2009: Image feature extraction based on BEMD and gray level co-occurrence matrix (in Chinese). Comput. Eng. Appl., 45, 201203.

    • Search Google Scholar
    • Export Citation
  • Nunes, J. C., , Bouaoune Y. , , Delechelle E. , , Niang O. , , and Bunel PH. , 2003: Image analysis by bidimensional empirical mode decomposition. Image Vis. Comput., 21, 10191026, doi:10.1016/S0262-8856(03)00094-5.

    • Search Google Scholar
    • Export Citation
  • Nunes, J. C., , Guyot S. , , and Deléchelle E. , 2005: Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach. Vis. Appl., 16, 177188, doi:10.1007/s00138-004-0170-5.

    • Search Google Scholar
    • Export Citation
  • Peura, M., , Visa A. , , and Kostamo P. , 1996: A new approach to land-based cloud classification. Track D: Parallel and Connectionist Systems, Vol. 4, Proceedings of the 13th International Conference on Pattern Recognition, IEEE, 143147.

  • Shan, S. M., , Hu J. N. , , and Li F. , 2007: Texture classification based on bidimensional empirical mode decomposition (in Chinese). Comput. Eng. Des., 28, 58005804.

    • Search Google Scholar
    • Export Citation
  • Singh, M., , and Glennen M. , 2005: Automated ground-based cloud recognition. Pattern Anal. Appl., 8, 258271, doi:10.1007/s10044-005-0007-5.

    • Search Google Scholar
    • Export Citation
  • Smith, R., , Walker D. , , and Schwarz H. E. , 2004: The Tololo all-sky camera. Scientific Detectors for Astronomy: The Beginning of a New Era, P. Amico, J. W. Beletic, and J. E. Beletic, Eds., Astrophysics and Space Science Library, Vol. 300, Springer, 379–384, doi:10.1007/1-4020-2527-0_46.

  • Song, Q. Q., , and Yu F. Q. , 2010: Application of Hilbert–Huang transform to fine time–frequency analysis of speech signal (in Chinese). Comput. Eng. Appl., 46, 149151.

    • Search Google Scholar
    • Export Citation
  • Sun, X. J., , Liu L. , , Gao T. C. , , and Zhao S. J. , 2009a: Classification of whole sky infrared cloud image based on the LBP operator (in Chinese). J. Nanjing Inst. Meteor., 32, 490497.

    • Search Google Scholar
    • Export Citation
  • Sun, X. J., , Liu L. , , Gao T. C. , , Zhao S. J. , , Liu J. , , and Mao J. T. , 2009b: Cloud classification of the whole sky infrared image based on the fuzzy uncertainty texture spectrum (in Chinese). J. Chin. Appl. Meteor. Sci., 20, 157163.

    • Search Google Scholar
    • Export Citation
  • Sun, X. J., , Wang X. L. , , Li H. , , Zhang W. X. , , and Yan W. , 2009c: Atmospheric Sounding Science (in Chinese). Meteorology Publishing House, 452 pp.

  • Wang, Z., , and Sassen K. , 2001: Cloud type and macrophysical property retrieval using multiple remote sensors. J. Appl. Meteor., 40, 16651682, doi:10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xiong, X. J., , Guo B. H. , , Hu X. M. , , and Liu J. J. , 2002: Application and discussion of empirical mode decomposition method and Hilbert spectral analysis method (in Chinese). J. Oceanogr. Huanghai Bohai Seas,20, 12–21.

  • Zhang, Y. H., , Lu X. F. , , Yuan Y. , , and Yu W. K. , 2010: Ground nephogram classification based on textural feature extraction and neural networks. The Second International Conference on Information Science and Engineering: Proceedings, IEEE, 37373740, doi:10.1109/ICISE.2010.5690096.

  • Zhao, C., , Wang Y. , , Wang Q. , , Li Z. , , Wang Z. , , and Liu D. , 2014a: A new cloud and aerosol layer detection method based on micropulse lidar measurements. J. Geophys. Res. Atmos., 119, 67886802, doi:10.1002/2014JD021760.

    • Search Google Scholar
    • Export Citation
  • Zhao, C., , Xie S. , , Chen X. , , Jensen M. , , and Dunn M. , 2014b: Quantifying uncertainties of cloud microphysical property retrievals with a perturbation method. J. Geophys. Res. Atmos.,119, 5375–5385, doi:10.1002/2013JD021112.

    • Search Google Scholar
    • Export Citation
  • Zhao, Z. D., , Tang X. H. , , Zhao Z. J. , , Pan M. , , and Chen Y. Q. , 2005: Spectrum analysis of heart sound signal based on Hilbert–Huang transform (in Chinese). Chin. J. Sens. Actuators, 18, 1822.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 17 17 7
PDF Downloads 8 8 5

Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

View More View Less
  • 1 School of Instrument Science and Engineering, Southeast University, and College of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China
  • 2 School of Instrument Science and Engineering, Southeast University, Nanjing, China
  • 3 College of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China
  • 4 School of Instrument Science and Engineering, Southeast University, Nanjing, China
© Get Permissions
Restricted access

Abstract

It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

Corresponding author address: Aiguo Song, School of Instrument Science and Engineering, Campus Sipailou, Southeast University, Nanjing 210096, China. E-mail: a.g.song@seu.edu.cn

Abstract

It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

Corresponding author address: Aiguo Song, School of Instrument Science and Engineering, Campus Sipailou, Southeast University, Nanjing 210096, China. E-mail: a.g.song@seu.edu.cn
Save