1. School of Electrical and Information Engineering,Anhui University of Technology,Maanshan　 243002,China; 2. Anhui Key Laboratory of Clean Conver- sion and Utlization,Anhui University of Technology,Maanshan　 243002,China; 3. Key Laboratory of Metallurgical Emission Recycling,Ministry of Education, Anhui University of Technology,Maanshan　 243002,China

在分析煤岩镜质组显微组分特点的基础上,针对其结构复杂、特征量多且相互交织从而影响分类准确性等问题,提出一种基于主成分分析(PCA)的煤岩显微组分镜质组分类方法。首先根据镜质组显微图像中各组分呈现的条状、团块、颗粒等纹理特点和亮度差异,采用基于灰度共生矩阵的能量、熵、惯性矩、局部平稳性等纹理特征量和基于灰度分布统计的亮度比、均值、均方差、三阶矩偏度等亮度相关特征量对其进行描述,构成初始特征量集;再采用主成分分析法对初始特征量集进行进一步的抽取;最后构建基于径向基函数的支持向量机(RBF-SVM),采用积累贡献率较大的主成分作为分类参量实现镜质组的自动分类。实验结果表明:纹理和灰度统计特征可有效刻画煤岩镜质组显微组分;采用PCA对初始特征进行抽取之后,用于分类的特征空间维数大幅度降低,分类算法的泛化能力增强,分类的准确率显著提高。

On the basis of analyzing the characteristics of macerals in vitrinite of coal,in view of the fact that the struc- tures of macerals are very complex and there are too many features that mix and makes classification difficult,a macer- al classification method based on principal component analysis ( PCA) is proposed. Firstly,according to the texture characteristics (strip,crumb,grain,etc) and intensity difference,macerals are represented with texture related features as energy,entropy,moment,local smooth based on gray level co-occurrence matrix and intensity related features as contrast,mean,standard deviation,3-order moment deviation based on the gray-level statistics of coal microscopic ima- ges,and a primary feature set is generated. Then,by using PCA,primary features are further selected and extracted. Fi- nally,a Support Vector Machine based on radial basis function (RBF-SVM) is built,and macerals are classified ac- cording to those principal components with greater cumulative contribution. Experimental results show that texture can present macerals feature of vitrinite effectively;with features extracted by PCA,the dimensions of feature space are greatly reduced,the generalization ability of classification algorithm is improved,and the accuracy rate of classification is obviously increased.

国家自然科学基金资助项目(51574004)；安徽省自然科学基金资助项目(1208085ME67)；

Wang Peizhen,Yin Zihuan,Wang Gao,et al. A classification method of vitrinite for coal macerals based on the PCA and RBF-SVM[J]. Journal of China Coal Society,2017,42(4):977-984.