  To improve the purity of coal and prevent damage to the coal mining machine,
it is necessary to identify coal and rock in underground coal mines. At the
same time, the mined coal needs to be purified to remove rock and gangue. These
two procedures are manually operated by workers in most coal mines. The
realization of automatic identification and purification is not only conducive
to the automation of coal mines, but also ensures the safety of workers. We
discuss the possibility of using image-based methods to distinguish them. In
order to find a solution that can be used in both scenarios, a model that
forwards image feature extracted by DenseNet to Gaussian process is proposed,
which is trained on images taken on surface and achieves high accuracy on
images taken underground. This indicates our method is powerful in few-shot
learning such as identification of coal and rock/gangue and might be beneficial
for realizing automation in coal mines.
