A classification method of vitrinite for coal macerals based on the PCA and RBF-SVM

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.