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Flow feature analysis and extraction for classifying axisymmetric vector field patterns

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Abstract

A relatively fixed structure vector field which includes clockwise vortex, anticlockwise vortex, convergence, divergence, and saddle is defined as axisymmetric vector fields (AVF) in this study. A method for characterizing and classifying the type of flow patterns in 2-dimension actual vector field for the meteorology application is proposed. First of all, the collected AVF samples are transformed to a directional pseudo-color image by means of mapping them to the hue component space in the HSL color model. Secondly, the directional hue difference and similarity degree to the normal AVF are respectively extracted as two features by the technique of image processing. Thirdly, two improved physical properties (vorticity and divergence) of AVF are introduced and improved for this study. Finally, in the experiment, the probability density distribution for every type of AVF samples is employed to analyze the four features advantages and disadvantages on the five AVF patterns classification. The correlation of the features is discussed by the PCA method. The statistics results show that the features are effective to describe the AVF patterns. By training a classifier based on constructing a decision tree, the classification ability of the features is proved on processing different scale and resolution AVF samples by comparing with traditional methods.

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Acknowledgments

The author would like to thank the Special Found for Meteorology Scientific Research in the Public Interest for financial support and to Tianjin Meteorological Station for the provision of wind fields data of European Centre for Medium Range Weather Forecasts and China Meteorological Center. This work is supported by National Natural Science Foundation of China (No. 71102174 and No. 51306058), the Fundamental Research Funds for the Central Universities (No. 2014QN46), the Hebei Science and Technology Support Project (No. 15212204D), the Tianjin Science and Technology Support Project (No. 15ZCZDNC00130), and State Scholarship Fund for Visiting Scholar by the China Scholarship Council (No. 201406735004).

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Authors and Affiliations

  1. School of Electrical and Automation Engineering, Tianjin University, Tianjin, 300072, China

    Jie Hou & Ping Wang

  2. Department of Automation, North China Electric Power University, Baoding, 071003, China

    Tao Gao

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  1. Jie Hou
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  2. Tao Gao
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  3. Ping Wang
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Correspondence to Tao Gao.

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Hou, J., Gao, T. & Wang, P. Flow feature analysis and extraction for classifying axisymmetric vector field patterns. Multimed Tools Appl 76, 14617–14634 (2017). https://doi.org/10.1007/s11042-016-3812-5

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  • DOI: https://doi.org/10.1007/s11042-016-3812-5

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