Recognition on low-level wind shear of wavelet invariant moments

According to the shape characteristic relationship within microburst, low-level jet stream, side wind shear and tailwind-or- headwind shear images, the feature extraction technique of wavelet invariant moment applied to the recognition of wind shear was mainly studied. Firstly, wavelet invariant moments method was employed to extract shape features of low-level wind shear images, which was based on cubic B- spline wavelet basis. Then, the feature dimensions were reduced by Fisher Linear Discriminative Analysis(LDA) in order to get the effective shape features of target images. Finally, the effective shape features were fed into 3-nearest neighbor classifier to identify four types of low-level wind shear. The experiment results demonstrate that the proposed approach has stronger robustness and better average recognition rate compared to the recognition effect based on Hu moment and Zernike moment, which can effectively be used to recognize the type of wind shear images.