Abstract: The portable spectrometer SVC HR-1024 was used to carry out spectral tests on the 92 bituminite and 58 lignite coal samples from various coal mines. By comparing their spectral curves， the differences between bituminite and lignite samples can be observed visibly in spectral characteristics. The reflectance of lignite samples is obviously higher than that of bituminite samples， as well as the slope of spectral curves. On the basis of spectral characteristics analysis， the MAO model algorithm， random forests， BP neural networks and ELM-neural network were selected for the classification of bituminite and lignite samples. The results indicated that the MAO model algorithm and random forest algorithm outperform other algorithms on classification. For large-area and rapid recognition by remote sensing， the MAO model algorithm has a great advantage in the classification time. While the random forest algorithm can be used for classification in small mining areas.

Key words: bituminite, lignite, visible and near-infrared, remote sensing, classification