Abstract
High-quality coal emits a smaller amount of harmful substances during the combustion process, which greatly reduces the environmental hazard. The sulfur content of coal is one of the important indicators that determine coal quality. The world's demand for high-quality coal is increasing. This is challenging for the coal mining industry. Therefore, how to quickly determine the sulfur content of coal in coal mining areas has always been a research difficulty. This study is the first to map the distribution of sulfur content in opencast coal mines using field-remote sensing data, and propose a novel method for evaluating coal mine composition. We collected remote sensing, field visible and near-infrared (Vis-NIR) spectroscopy data and built analytical models based on a tiny neural network based on the convolutional neural network. The experimental results show that the proposed method can effectively analyze the coal sulfur content. The coal recognition accuracy is 99.65%, the root-mean-square error is 0.073 and the R is 0.87, and is better than support vector machines and partial least squares methods. Compared with traditional methods, the proposed method shows many advantages and superior performance.