基于K-L变换的污秽绝缘子红外图像特征提取方法

Characteristic Extraction Method of Contaminated Insulator Infrared Image Based on K-L Transform

利用K-L变换降低污秽绝缘子红外图像特征维数,得到3个独立主分量特征,该3维主分量包含了各原始特征分量的信息。通过主分量累积贡献率的大小,选取了原始污秽特征数据协方差矩阵的3个最大特征值对应的3维特征矢量构成正交变换阵,用于主分量特征提取。通过正交变换阵中各系数的大小,研究了各原始特征分量在各主分量特征中所占的比例。为比较K-L变换前后数据类间距离的变化,绘制了K-L变换前后特征数据分布的3维立体图。实验结果证明K-L变换在最大限度地保留原有信息的同时,降低了原始污秽特征数据的维数,减少了计算量,加大了数据的类间距离,提高了污秽数据分类的准确性。

The K-L （Karhunen-Loeve） transform is applied to reduce dimensions of the characteristics extracted from the contaminated insulator infrared image. Three independent principal components including information of every original characteristic are then obtained by the K-L transform. An orthonormal matrix composed of three eigenvectors which correspond to three maximal eigenvalues of the covariance matrix of the original characteristic data is selected by the principal component cumulative contribution proportion and thus used to extract the principal components. The data among the orthonormal matrix denote the proportion of every original characteristic in the principal components. In order to compare the changes of the space distances between classes of the original characteristic data classes and the principal component characteristic data classes, the three-dimensional figures imaging the characteristic data distribution before and after the K-L transform are illustrated. Finally, experimental results indicate that the K L transform can reduce the dimensions of the original contaminated data while to a great extent preserving the information of the original characteristic data, also, lessen the calculation, increase the distances between classes, and improve the accuracy of data classification.