# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 1 Introduction
Among many textural methods, a grey-level cooccurrence matrix (GLCM) is the most popular technique for the computation of second-order textural features [17]. However, spectral and spatial features derived from Sentinel-2 data are rarely integrated into lithological mapping in current study. These scenarios constitute the motivation for this study, which explores an efficient way to integrate the spectral and spatial data of Sentinel-2 in lithological mapping. To effectively derive reliable lithological information from remote sensing data, appropriate classification techniques are essential. Machine learning classifiers could be widely divided into parametric (preferred with normally distributed data) or non-parametric types. The maximum likelihood classifier (MLC) is a typical parametric classifier. Over the decades, it has been widely applied in lithological mapping [18]. MLC labels the pixels in an image according to user-defined classes based on posterior probabilities computed from the statistics of training data. MLC is an optimal classifier if the training data is normally distributed. However, obtaining normally distributed training data is often difficult in practice [19]. In the last decades, various non-parametric classification algorithms have been developed in order to obtain more accurate and more reliable classifications. Among them, RF and SVM achieved better performance and were widely used in lithological mapping [20; 21]. The SVM has been increasingly used in the remote-sensing community due to its ability to generate good classification results even with limited and spectral-mixed training data, a common limitation for traditional digital image classification [22]. The RF classifier has been gaining increasing attention because it has fast computation, few parameter requirements, few statistical assumptions on training data, less sensitivity to noise or over-fitting, and the capability to determine variable importance [23; 24]. Inthe supervised classification field survey, numerical and geostatistical analyses with the help of GIS were usually applied to determine the boundary of different rock units, select the training and test datasets, and evaluate the classification accuracy via the producer accuracy, user accuracy, average accuracy (the average of user and producer accuracy), overall accuracy and Kappa coefficient. Feature selection can be considered as an important pre-processing step for classification, potentially improving performance and efficiency of the classifier [25]. With the increase in feature dimensions, the lithological mapping model will likely become more complex and the classification accuracy will be influenced by data redundancy. Efficiency, accuracy and feature selection can be jointly utilised during classification to balance the complexity of models. The existing feature selection methods can be divided into filters, wrappers and embeddings [26; 27; 28]. Filter methods such as relief and relief, first selects the subset of all features that is independent of the learning processing. These methods have been widely adopted in feature selection owing to their simplicity and efficiency [29]; however, the features selected by this filtering method may not achieve good performance in subsequent classification tasks. Wrapper methods, such as feature elimination [30], simulated annealing [31] and genetic algorithm (GA) [32], directly take the performance of the model as an evaluation criterion of the feature subset, therefore, they are better than the filter methods. However, the wrapper approach has high computational costs due to the process of multiple executions [33]. Embedded methods, such as RF [34], perform feature selection in the process of training and are usually specific to given learning machines. Therefore, the search for an optimal subset of features is built into the classifier construction and can be seen as a search in the combined space of feature subsets and hypotheses.