基于仿真雷达图像的低空风切变类型识别研究

摘要

Simulation are made to identify different types of low-level wind shears by extracting texture features based on wavelet transform and using BP neural network. Firstly the simulated radar scan images are generated by using the existing simulated radar data,then the wind shear regions are extracted by threshold segmentation;The following step is two levels wavelet decomposition on it; The feature vectors are obtained from the mean and standard deviation of each sub-band's wavelet coefficients. Finally,BP neural network is used to get the classification results by identifying the eigenvector inputted. The simulation results show that the algorithm has a good feasibility.%采用基于小波变换提取纹理特征和BP神经网络对低空风切变的类型识别进行仿真研究.利用已有的仿真雷达数据生成仿真雷达扫描图像,通过阈值分割提取风切变区域,之后对其进行二层小波分解,求取各子带小波系数的均值和标准差作为特征向量.最后利用BP神经网络对特征向量进行识别分类.仿真结果比较理想,表明算法具有良好可行性.