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A majority of air crash is caused by low-level wind shear. That can affect the direction and velocity of the aircrafts. So, it is very necessary to recognize the low-level wind shear in a short time to make the early warning. In this article, we propose a recognition method which uses support vector machine to make the classification of the low-level wind shear images measured by laser detection and ranging. We use the partial scanning images instead the traditional whole scanning ones, so that it can decrease the calculation time and avoid the wind field inversion. The feature exacting methods we use are invariant moments and gray-gradient co-occurrence matrix. They can, respectively, catch 7 features and 15 features of the wind velocity distribution images. At the same time, we use support vector machine that the parameters are optimized by K-fold cross-validation to do the pattern recognition. Moreover, the simulation results of recognition are given.

Recognition of partial scanning low-level wind shear based on support vector machine