# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 6 Conclusions  
This study evaluated the capability of Sentinel-2 data for lithological mapping by producing and testing various integrations of the raw and derived Sentinel-2 data in terms of their role in improving the lithological mapping accuracy. By means of advanced GIS and remote sensing techniques, the distribution boundaries of rock units were determined. The spectral and spatial information of different rock units were achieved from remote sensing data as training and test datasets for lithological mapping. The Relieff-RF model was employed to determine the optimal dataset for lithological classification. The lithological mapping accuracy of Sentinel-2 had been significantly improved by the optimising model in this study. The results were as follows:  
(1) The classification accuracy of Sentinel-2 (60.71%) was 4.77% higher than that of ASTER and 5.24% higher than that of Landsat-8 OLI. Sentinel-2 performed better than ASTER and Landsat-8 OLI in lithological mapping.  
(2) The selected image enhancement methods, namely, PCA, MNF and ICA, could increase the quality of input datasets and improve the classification accuracy. The highest classification accuracy of 65.21% was achieved using ICA. Textural features, when used independently, failed to improve the classification accuracy (i.e., lowest classification accuracy of 58.87%). After integrating the raw data of Sentinel-2, the overall accuracy  
Figure 13: Partial magnification of misclassification. (**a**) A residential area on Sentinel-2; (**b**) misclassification result of a residential area; (**c**) gullies on Sentinel-2; (**d**) misclassification results of gullies.The misclassification areas were outlined by red boxes.  
improved significantly (75.75%). In addition, the classification result of RF achieved better performance than that of SVM.  
(3) Feature selection is an important pre-processing step for dimensionality reduction in lithological mapping. In this study, the Relieff-RF model was used to optimise datasets; it effectively reduced redundancy and improved operation efficiency and classification accuracy. The use of 20 variables selected by the Relieff-RF model resulted in an overall accuracy of 77.63%, which was 1.88% higher than the combination dataset of 114 variables.  
The problem of data redundancy caused by the increase in feature dimensions still needs to be explored. In future work, multisource remote sensing data can be used in lithological mapping to explore a more efficient and accurate lithological method, especially in vegetation covered areas.  
Conceptualisation, J.X.; methodology, J.X. and Q.J.; software, J.X. and H.L.; validation, Q.J. and X.G.; formal analysis, J.X. and X.G.; resources, J.X. and Q.J.; writing--original draft preparation, J.X.; writing--review and editing, J.X. and H.L.; supervision, Q.J. and X.G.; and project administration, Q.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"Remote Sensing Geological Survey Project of Global Critical Zone in China\" (DD20190536).  
Not applicable.  
This study did not report any data.  
The authors are grateful to the European Space Agency (ESA) for providing Sentinel-2 data.  
The authors declare no conflict of interest.