## 5 Conclusions  
Inspired by the way that petrologists examine photomicrographs, we proposed an automatic and effective framework for maceral classification. The proposed strategy is fundamentally different from previous attempts to classify a region and classify the central pixel of a ROI. Based on the image segmentation, rather than the fixed-size window, the regions of interest are cropped automatically. Current fixed-size window-based strategies, including both patch-wise classification and pixel-wise classification, neglect the shape and the size information and therefore do not work in identifying maceral composition from one entire photomicrograph. The utilized two-level coarse-to-fine clustering strategy achieved significantly better segmentation results as compared to the corresponding single-level clustering. In addition, considering the complicated nature of coal, it may prove difficult to identify maceral components based on a single kind of features. Our results suggest that classification approach based on multiple kinds of features, such as geometric, grayscale and texture features, will be a promising direction for identifying maceral components.  
However, it should be stressed that the research described in this paper is a preliminary study which still has some limitations. First, the proposed method only works when the degree of coalification is smaller than 1.0%. With the increase of that degree, it will be difficult to distinguish vitrinite and liptinite based on gray scale values. Second, we assume that there are four maceral groups of each sample. However, this assumption may not hold in a few cases. We have tried density-based clustering methods, such as density-based spatial clustering of applications with noise (DBSCAN), which can automatically detect the optimal number of clusters. However, the performance is not better than the proposed method. We will investigate this issue in the future. In addition, 39 photomicrographs used in this study were analyzed according to definitions in ASTM Standard D 2799-13 and atlas in [39]. There are seven types of maceral components, belonging to three maceral groups (i.e., vitrinite, liptinite and inertinite). Liptinites include sporinite and cutinite. Inertinite macerals include fusinite, semifusinite, inertodetrinite, and macrolite. However, the mineral components and the other macerals, such as funginite or macrolite, are rarely found in these photomicrographs, and are not abundant in nature. Therefore, in this study, we do not consider these components, and follow a simplified classification as that in [39]. The proposed MISC strategy obtained relatively good performance, especially when we take into account a high degree of complexity of coals. Although with some limitations, to the best of our knowledge, our work is the first study aiming to provide complete analysis of maceral composition.  
Figure 8: The user interface of MISC for automatic coal petrographic analysis.  
**Author Contributions**: Conceptualization, H.W. and L.Z.; funding acquisition, M.L. ([PERSON]) and L.Z.; methodology, H.W. and Y.C.; project administration, M.L. ([PERSON]); resources, M.L. ([PERSON]); supervision, M.L. ([PERSON]) and L.Z.; validation, Y.C. and M.L. ([PERSON]); writing--original draft, H.W.; writing--review and editing, L.Z.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities with grant number 2019 ZDPY17.  
**Conflicts of Interest**: The authors declare no conflict of interest.