# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 1 Introduction  
Lithological mapping is an important application of remote sensing technology in geology. Remote sensing data contain rich spectral and spatial features that can provide various information to produce new geological maps or update existing geological maps, especially inaccessible areas. Due to its lower cost and higher accessibility, moderate-resolution remote sensing multispectral data, such as Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI) and ASTER, have been successfully used in geological surveys and mineral exploration [1; 2; 3]. Compared with Landsat and ASTER, Sentinel-2 has rarely been applied in lithological mapping. As one of the most important data continuity and enhancement projects for the earth observation, more case studies are necessary to better demonstrate the capabilities and potential of Sentinel-2 for lithological mapping. Sentinel-2A/B satellites, an extension of the Earth observation missions of the Landsat and SPOT satellites, were launched with a multispectral imaging instrument by the European Space Agency via the Copernicus Programme in 2015 and 2017, respectively. The mission ensures continuity of the SPOT and Landsat missions and provides operational products such as land cover maps, land change detection maps and geochemical/physicalvariables [4]. The subtle differences of various ground objects can be distinguished by the 13 narrow bands (4 bands at 10 m, 6 bands at 20 m and 3 bands at 60 m) of Sentinel-2 data, which span from the visible and near-infrared bands to the shortwave infrared band [5]. Furthermore, Sentinel-2 has a short revisit time (every five days for two satellites) and Sentinel-2 scenes in the data archive are made available to all users at no cost. In the existing research, [PERSON] et al. [6] utilised a Sentinel-2A imager and ASTER to discriminate lithological units in the ophiolite complex of Shibanjing in Inner Mongolia, China. The results shown that Sentinel-2 data outperformed ASTER in lithological mapping in the Shibanjing ophiolite complex. In addition, until April 2008, the SWIR subsystem of ASTER had a failure of the detector cooling apparatus (not the Stirling cycle cooler), so no further SWIR data could be captured [7]. [PERSON] et al. [8] provided a comprehensive review of the use of Landsat-8 and Sentinel-2 in mineral exploration. By comparison and analysis, the Sentinel-2 data have shown potential, especially in mapping iron absorption features, due to the similar or even higher spectral characteristics than the Landsat series and SPOT in the VNIR part. It is shown fully that, based on the spatial and spectral characteristics proposed for Sentinel-2 and its similarity to Landsat ETM, SPOT and to lesser extent ASTER, there seems to be great potential of developing products that can be used for the geology community [9]. Furthermore, some researchers have used commonly processing methods such as principal component analysis (PCA), independent component analysis (ICA), minimum noise fraction (MNF) and multi-source data with Sentinel-2 to improve the lithological classification accuracy and achieved better performance [10; 11; 12]. For example, [PERSON] et al. [13] combined PCA, MNF and ICA to enhance the lithological mapping accuracy. For Sentinel-2A, the lithological mapping accuracy reached 93.93%. [PERSON] et al. [14] integrated gamma ray spectrometric data with Sentinel-2 data, leading to monitoring minute variations among 13 classes and discrimination of closely related spectral signatures. The results shown that blending the total count band (K + Th + U) with Sentinel 2 data outstandingly boosted the classification accuracy by 7.77%. Texture is one of the major visual properties of rock units. Spectral features (reflectance) capturing textural features of lithologies that may be unique as a result of differences in physical weathering erosion and drainage, can provide useful supporting information to distinguish rock types [15; 16].