Flow feature analysis and extraction for classifying axisymmetric vector field patterns

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.