# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 4 Methodology  
### Pre-processing of the Satellite Images and Extraction of Spectral Signatures  
The satellite images used in this research were acquired in clear sky conditions, with minimum atmospheric disruptions. A single image was used for each region; therefore, an atmospheric correction pre-processing step was not necessary [50]. The spatial resolution of the Sentinel-2 image bands varied through the wavelength portions. Thus, there was a need for uniform spatial resolution for analyses such as a point-based spectral profile generation, spectral index generation, and multispectral image classification. [PERSON] et al. [51], had performed a comparative analysis to evaluate the effects of downscaling to 10 m resolution and upscaling to 20 m resolution on land-cover and land-use classification with Sentinel-2 images. They asserted that the upscaled 20 m resolution images provided the lowest classification accuracies, due to a loss of spatial details and an increase in the number of mixed pixels, by combining four adjacent pixels. Their results revealed that downscaling to a 10 m resolution with the nearest neighbour resampling algorithm, improved the classification, and they recommend this approach for unifying the spatial resolution. [PERSON] [52], had asserted that downscaling to a fine resolution is more suitable for an LCU classification by completely utilizing the detailed information from high spatial resolution bands. Based on the above findings, the 20 m and 60 m resolution bands of the Sentinel-2A imagery were resampled to 10 m, by using the nearest neighbour method, to maintain the spatial resolution integrity. This resampling algorithm has been widely used, due to its easy implementation and spectral information conservation [53]. In the next step, spectral profiles of different land object types, including broadleaf forest, deciduous forest, farmland, urban green cover, built-up, industrial region, sparse built-up region, seawater, lake water, asphalt, and bare land, were extracted to examine and compare the separation capacity of the Sentinel-2A image bands (Figure 2). During the classification process, samples from broadleaf and deciduous forest were assigned to the forest class, samples from urban green cover and farmland were assigned to the vegetation class, and samples from lake and sea were assigned to the water class, to obtain the seven land cover types used in this research. As Figure 2 illustrates, bare land had a similar reflectance to built-up areas and it was difficult to identify these two categories using a single index. It was simple to determine water bodies from other land cover types, due to their unique spectral signature. The gradual decrease of reflectance from band 1 to band 12 was specific to water bodies. A significant reflectance increment in the red-edge bands (B5, B6, B7) and NIR bands (B8, B8a), compared with the red band (B4) was specific to vegetation cover and could be utilized to detect vegetated regions. Additionally, the reflectance curve analyses proved that B1, B9, and B10 (60 m native resolution) could not be used to separate the land cover classes. These observations could be explained by the characteristics of these bands. B1 (coastal aerosol), strongly influenced by the atmosphere and by B9 and B10, which were water vapour and cirrus, did not provide spectral information about the Earth's surface ([PERSON], 2014). Thus, these bands were removed from the data and further analyses was performed with the remaining 10 bands. The spectral evaluation showed that the main challenge was to separate the bare land and the built-up areas, which was the principal objective of this research. Therefore, it aimed to evaluate the existing built-up indices as a first step and proposed an alternative index for those cases that had a lower efficiency.