Separation Between Coal and Gangue Based on Infrared Radiation and Visual Extraction of the YCbCr Color Space

Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes. We analyze the infrared images of coal and gangue; then extract the appropriate texture features from the infrared images in order to develop an accurate classification system by using support vector machine (SVM). The method applied in this work essentially depends on feature extraction of images. The statistical features based on gray level information (GLI), grey-level cooccurrence matrix (GLCM) and visual features are executed. Thus, we suggest preparation steps to obtain one select feature before importing the data into the SVM classifier, and this approach is adopted as the fundamental basis for our work. We exploit only one feature of the infrared image, namely, Cb, which is extracted from the YCbCr color space, and then compute the mean value of Cb after heating and capturing the photos for the coal and gangue samples. The proposed method achieves a high classification accuracy 97.83 & x0025; by using Gaussian-SVM.