小波不变矩的低空风切变识别  

Recognition on low-level wind shear of wavelet invariant moments

作　　者：蒋立辉[1,2] 陈红[2] 庄子波[1] 熊兴隆[2] 

机构地区：[1]中国民航大学民航气象研究所,天津300300 [2]中国民航大学天津市智能信号与图像处理重点实验室,天津300300

出　　处：《红外与激光工程》2014年第11期3783-3787,共5页Infrared and Laser Engineering

基　　金：国家自然科学基金(41075013);中央高校基金(ZXH2010D020;3122013P009);国家重点基础研究发展计划(973计划)(2010CB731800)

摘　　要：针对微下击暴流、低空急流、顺逆风以及侧风低空风切变样本图像间的形状特性关系,主要研究了小波不变矩的特征提取技术在风切变识别中的应用。首先,采用基于三次B样条的小波不变矩提取风切变图像的形状特征。然后,将提取的特征通过Fisher线性判别分析(LDA)降低维数,实现风切变有效特征的提取。最后,采用三阶近邻分类器分类识别四种低空风切变。实验结果表明,该算法与应用Hu矩和Zernike矩特征进行分类识别相比,识别结果更加稳定,且平均识别率得到了较大提高,能够有效用于风切变图像的类型识别中。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.

关 键 词：低空风切变 小波不变矩 三次B样条 形状特征 线性判别分析 

分 类 号：TN958.98[电子电信—信号与信息处理]