基于各向异性核扩散法的杨树叶特征降维

Dimensionality Reduction for Poplar Leaves Features Based on Anisotropic Kernel Diffusion Map

对缺水与正常杨树苗叶片进行特征分析与降维处理。首先对样本进行光照补偿并去除奇异性;然后对样本数据空间进行归一化处理,提出采用基于各向异性核扩散法对缺水与正常样本数据空间进行降维,核参数采用最大类间距离法自适应调整;最后根据最大信噪比原则选择降维子空间维数,获得识别特征。分别对各向异性核扩散法、LE、LTSA以及PCA进行分析比较,对于叶脉较粗的杨树叶片,采用各向异性核扩散法效果较好,能保持空间的几何关系。采用SVM分类法对不同算法提取的特征进行分类,结果表明本文提出的算法提取的杨树叶特征分类效果较好。

Dimensionality reduction approach was proposed based on anisotropic kernel diffusion map to extract the features of poplar leaves, in which the kernel parameters were adjusted adaptively. In order to improve the accuracy and efficiency, singularity points were removed and features normalization method was employed to obtain the robust features. The maximum margin criterion method was utilized to obtain anisotropic kernel parameter by gradient descent method. The results show that the anisotropic kernel diffusion map has good performance on efficiency for poplar leaves compared with LE, LTSA and PCA. The comparisons of classification experiments have been conducted, by using SVM （support vector machine） classifier to recognize the water shortage of poplar leaves, and the results validate the accuracy and stability of the proposed method.