# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
###### Abstract
SVM also provided better performance when applied to multi-index images than the Neural Network (NN) classifier and regression trees (CART) [18]. In a recent article, [PERSON] and [PERSON] summarized the recently developed advanced SVM-based classification approaches used in remote sensing studies. They concluded that SVM-based classification methods perform better in terms of accuracy, speed, and memory requirements, and can operate effectively and accurately in cases where training samples are limited, which is generally the case for satellite image classification problems. They also noted the constraints that should be considered in SVM, such as the need to appropriately define the kernel function, the representation efficiency of the training sample, and the consistency of statistical distributions between classes [19]. In most satellite image classification scenarios, higher accuracies (over 85%) are attained when the major land cover (LC) classes, such as vegetation, water, and urban classes are the concern [20; 21]. Achieving high accuracies becomes challenging when the higher-level class definitions are the concern, due to the spectral and spatial similarities [22]. According to a survey done by [PERSON] et al., the classification accuracies achieved by supervised algorithms vary greatly, due to the number of training samples and proprieties of selected features for classification [23]. These findings indicate a feature selection problem that occurs regardless of the classification algorithm. An increased number of features, such as the image bands, generally improves the accuracy; however, it increases the amount of training samples required [24]. Considering the limited training sample availability in most conditions and the nonlinear response of the LCU classes across several bands, which is known as the \"[PERSON] effect\" [25], there is a need for a method to select a subset of relevant features from the original dataset to improve the classification process and achieve a dimension reduction [26]. One alternative to overcome the above-mentioned problem is to perform linear transformation methods (such as principal components analysis (PCA) and independent component analysis (ICA)), or nonlinear algorithms (such as locality adaptive discriminant analysis (LADA) and multiple marginal fisher analysis (MMFA)), to remove the correlations and higher-order dependences in the image bands and use the produced components as input data for classification, to simplify and improve the process. The linear methods have been widely applied on multispectral data, however, nonlinear methods are generally applied on hyperspectral test data or natural image-based applications, such as face recognition [27; 28; 29; 30]. Spectral indices derived from satellite images can be used as an alternative data source for LCU characterization [31; 32; 33; 34]. The characteristics of the reflected energy in different regions of the spectrum for a specific land property, can be utilized to produce various indices. Using the spectral indices for LCU mapping is an operational approach as it enables LCU mapping at a higher degree of accuracy, which is highly comparable to those from a complex interpretation of quantity [35; 36]. [PERSON] et al. [37], for the first time, introduced an automated index-based method for mapping the built-up regions. Nevertheless, there is a significant drawback that should be considered when using spectral indices for LCU mapping. Some land features, such as water bodies and vegetation cover have very specific spectral reflectance characteristics, which facilitate the separation from other features, using spectral indices. However, it is challenging to detect built-up areas and effectively separate them from bare lands using a single index, due to similarities in the spectral characteristics. Confusion over, and misclassification of, built-up areas and bare lands is a problem, which can be partially addressed by the available built-up, index-based analyses [37; 38; 39].