# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 1 Introduction  
Numerous studies have demonstrated the ability to exploit the spectral features of minerals and rocks from the reflected solar spectra for lithological mapping and mineral exploration. Based on the spectral features, from visible to near-infrared (VNIR) wavelengths, some iron-bearing minerals, such as goethite and hematite, have been found in prior research to be relatively easy to distinguish [1; 2]. Because of the fundamental vibrations of Al-OH, Mg-OH, and CO\({}_{3}\)\({}^{2-}\) bonds in shortwave infrared (SWIR) wavelengths, many alteration minerals such as carbonates, sulfates, hydroxides, and oxides have been successfully identified and mapped using remote sensing methods [1; 2; 3; 4]. In thermal infrared (TIR) wavelengths, igneous rocks with relatively high SiO\({}_{2}\) contents have diagnostic emission spectral features due to the vibration of the Si-O bond, based on which, some rock indices have been constructed, such as the sulfuric acid rock index and the carbonate rock index [5; 6; 7]. Spectral-feature-based approaches for lithological identification mainly focus on the conversion and enhancement of spectral features, such as principal component analysis (PCA), spectral angle mapping (SAM), band ratio (BR), relative absorption band depth (RBD), false color composite (FCC), matched-filtering, and combinations of these methods [8; 9; 10]. However, there are still numerous limitations for lithological identification merely based on spectral features from VNIR to SWIR wavelengths. It is common for different surface objects, as well as homogeneous objects, to have similar spectral features, and many objects do not have prominent diagnostic spectral features. For example, it is difficult to precisely identify serpentinite and granite using only spectral features, as they are often intermingled with other rocks [11]. In terms of TIR data, the spectrum is generally dominated by temperature, which results in TIR radiance containing both temperature and emissivity information. The algorithms to separate temperature and emissivity are generally complex and problematic because of the nonlinearity in the relative contributions of temperature and emissivity effects [12; 13; 14]. The extracted emissivity products have a relatively high uncertainty and low contrast, which may subdue spectral features and limit the discrimination accuracy [15; 16]. A rock index is generally sensitive to a specific rock or mineral and is not enough to identify other rocks [4; 6]. Further, the application of spectral features is less successful for lithological mapping in vegetated terrain, as the bedrock is often partially or completely occluded by vegetation [17; 18]. In brief, fine lithological recognition is still difficult in geological remote sensing fields using only spectral information. Additional non-spectroscopic parameters, clues, and features aside from spectral information should be noted in lithological identification [19; 20; 21; 22; 23]. Numerous studies have demonstrated that image classification may be improved when additional information about spatial variation or surface morphology is incorporated in the classification procedure [8; 21; 24; 25]. The use of textural features as supplementary information for spectral information has drawn increasing attention in object identification and image classification [25; 26; 27; 28; 29]. Texture is the significant arrangement of pixels that generates regular spatial patterns [30] and determines the overall roughness or smoothness of surface objects. Different surface objects have different surface morphology, which yields different textural features. Textural features have been widely applied in several studies, such as sea ice mapping, crop species composition classification, vegetation structure analysis, lithological discrimination, and land-cover mapping [20; 30; 31; 32; 33; 34; 35; 36]. However, these studies focused more on the improvement or design of texture-derived methods, and texture features were calculated from a single remote sensing image.