CAJ | 학술논문

针对微下击暴流、低空急流、顺逆风和侧风4种不同低空风切变的激光雷达扫描图像，提出一种基于形状特征和纹理特征相结合的识别方法。采用Zernike矩和旋转不变统一模式的局部二值模式（LBP），分别提取反映风场全局变化的形状特征和反映风场局部变化的纹理特征；将两种特征串联融合后，通过主成份分析（PC A ）对其进行降维，提取有效特征；利用 k近邻分类器对4种低空风切变图像进行分类。实验结果表明，与其它多种算法相比，该算法平均识别率最高，识别效果更加稳定。
침대미하격폭류、저공급류、순역풍화측풍4충불동저공풍절변적격광뢰체소묘도상，제출일충기우형상특정화문리특정상결합적식별방법。채용Zernike구화선전불변통일모식적국부이치모식（LBP），분별제취반영풍장전국변화적형상특정화반영풍장국부변화적문리특정；장량충특정천련융합후，통과주성빈분석（PC A ）대기진행강유，제취유효특정；이용 k근린분류기대4충저공풍절변도상진행분류。실험결과표명，여기타다충산법상비，해산법평균식별솔최고，식별효과경가은정。
For four different low‐level wind shears of laser radar scan images which are microburst ,low‐level jet stream ,head and tail wind shear and side wind shear ,a recognition method based on shape and texture features was proposed .Firstly ,shape features reflecting the global changes of wind field and texture features reflecting the local changes of wind field were extracted using Zernike moments and rotation invariant uniform local binary pattern (LBP) .Then ,the two features were combined in se‐ries and the combined features were reduced by principal component analysis (PCA) to get the effective features .Finally ,k‐nea‐rest neighbor classifier was used to classify four types of low‐level wind shear images .The experimental results show that com‐pared with other algorithms ,the proposed algorithm has the highest average recognition rate and it is more stable .So this algo‐rithm can identify four low‐level wind shears effectively .