## 1 Introduction
### Related Work  
Automatic geological identification is becoming an increasingly important technique in various fields, such as in mining and geology. [PERSON] et al. presented a novel strategy to estimate the liberation spectrum from optical micrographs via random forest [8]. [PERSON] et al. proposed an autonomous classification method of rock images via unsupervised feature learning [9]. In [10], transfer learning was employed to deal with the problem of cross-region microscopic sandstone images classification. Numerous attempts have been made on the classification of microscopic rock images and achieved great success [11; 12; 13]. Considering the heterogeneous nature of coal, automatic identification of maceral components is still a challenging task [3]. In the early stage, the analytical methods to estimate the volume proportions of coal macerals were mainly based on the gray scale value of pixels [14; 15] and provided interesting results. However, the liptinite and background resin have similar gray scale values, and therefore it is difficult to separate them. In addition, different maceral components in a maceral group differ only subtly in term of the reflectance, and the existing methods merely based on gray scale values are not suitable for distinguishing maceral components. Furthermore, the gray scale values of a specific component may vary over a large range with the degree of coalification. Although the gray scale descriptions of pixels remain important, the need for more quantitative features, such as shape and texture, from photomicrographs has been recognized. With the development of machine learning and image processing, it is possible to automatically make more elaborate classifications [12; 16]. Over the past several years, attempts based on machine learning techniques have shown promising results in maceral analysis. [PERSON] et al. utilized principal component analysis (PCA) to extract primary features from texture-related and intensity-related features, and employed Support Vector Machine (SVM) to classify maceral components of the vitrinite group [17]. [PERSON] et al. selected 10 textural features via PCA and developed a novel strategy for automatic identification of macerals of the inertinite group. The proposed method achieved an outstanding accuracy of 93.6% based on a group of neural networks [16]. Most of the works focus on a single maceral group. So far, attempts to provide full identification of macerals have been considerably limited. [PERSON] and [PERSON] evaluated the ability of three machine learning methods for identifying three maceral groups of coal and non-organic minerals [3]. They cropped the region of interest (ROI) of 41 px \(\times\) 41 px and determined the label of the central pixel. Considering the morphological gradients along with the gray level features, the proposed method achieved an accuracy of 97% in classifying maceral groups. Furthermore, they analyzed six kinds of macerals of the inertinite group and an obtained accuracy is over 91%. Pearson and CSIRO have released two automated tools to identify maceral composition, including Pearson Coal Petrography (Pearson Coal Petrography--[[http://www.coalpetrography.com/blog1/](http://www.coalpetrography.com/blog1/)]([http://www.coalpetrography.com/blog1/](http://www.coalpetrography.com/blog1/))) and CSIRO coal grain analysis (CGS) (Coal Grain Analysis--[[https://www.csiro.au/en/Do-business/Commercialisation/](https://www.csiro.au/en/Do-business/Commercialisation/)]([https://www.csiro.au/en/Do-business/Commercialisation/](https://www.csiro.au/en/Do-business/Commercialisation/))Marketplace/Coal-Grain-Analysis), which have been successfully commercialized. However, they do not mention the detailed technologies employed in these two tools on the corresponding websites. Despite providing inspiring performance via machine learning-based methods, there are many issues that require more scientific breakthroughs.