Coal–rock interface detection on the basis of image texture features

Based on the stability and inequality of texture features between coal and rock, this study used the digital image analysis technique to propose a coal–rock interface detection method. By using gray level co-occurrence matrix, twenty-two texture features were extracted from the images of coal and rock. Data dimension of the feature space reduced to four by feature selection, which was according to a separability criterion based on inter-class mean difference and within-class scatter. The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm. In the optimized low-dimensional feature space, the coal–rock classifier was set up using the fisher discriminant method. Using the 10-fold cross-validation technique, the performance of the classifier was evaluated, and an average recognition rate of 94.12% was obtained. The results of comparative experiments show that the identification performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.