# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 6 Conclusions  
Separating bare land from the impervious surfaces and built-up areas has been the main problem in mapping urbanized areas. In this research work, a novel multi-index approach has been proposed for the LCU classification of Sentinel-2A satellite images, focusing on separating the urban and bare land, in addition to other land cover categories. To improve the classification accuracy and solve the misclassification and overestimation problems, a methodology was developed using spectral indices that categorized the three major land cover classes, water bodies, vegetation cover, and built-up areas. The multi-index images created with different index combinations were classified using the machine-learning-based SVM algorithm. The multi-index classification results were compared with the SVM classification result of the ten-band Sentinel-2A image. The results of this research showed that NDTI in combination with NDVIre and MNDWI improved the separation between the built-up regions and bare land, and significantly improved the misclassification of bare lands as built-up regions. In addition, the NDTI, which was calculated by the difference of the SWIR bands, divided by its sum of them, could be applied to the Landsat 5, 7, and 8 images, as well as the Landsat missions that included the SWIR bands with a similar wavelength range of the Sentinel-2 mission. This applicability enabled a further analysis using combined historic archival Landsat missions and higher spatial and spectral resolution Sentinel-2A images, to detect LCU changes through decades. Although classification of the ten-band Sentinel-2A imagery provided acceptable results related to the built-up area and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(Sentinel-2A Image (10 Bands))** & **(NDBI+NDVIre+MNDWI)** & **(NDTI+NDVIre+MNDWI)** \\ \hline
**Land Cover** & **Producer’s** & **User’s** & **Producer’s** & **User’s** & **Produce’s** & **User’s** \\
**Class** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline
**Bare land** & 21.13 & 77.77 & 34.95 & 40.56 & 91.05 & 77.24 \\
**Asphalt** & 96.42 & 47.36 & 46.42 & 37.14 & 96.42 & 75.00 \\
**Water** & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\
**Industry** & 68.18 & 96.77 & 61.36 & 96.42 & 84.09 & 100.00 \\
**Built-Up** & 77.77 & 50.90 & 53.70 & 42.02 & 70.37 & 89.41 \\
**Forest** & 45.00 & 69.23 & 35.00 & 70.00 & 95.00 & 86.36 \\
**Vg. Cover** & 78.26 & 54.54 & 82.60 & 65.51 & 86.95 & 95.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification accuracies of different approaches for the Konya region. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Overall Accuracy** & **Kappa** \\ \hline Sentinel-2A image (10 Bands) & 0.57 & 0.45 \\ Multi-index (NDBI+NDVIre+MNDWI) & 0.49 & 0.33 \\ Multi-index (NDTI+NDVIre+MNDWI) & 0.84 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the overall accuracy and Kappa statistics for the Konya region. bare land classes, the multi-index image of the NDTI, the NDVIire, and the MNDWI provided more interpretable and illustratable results for the built-up regions, in terms of shape, intensity, and pattern. The proposed combination also provided satisfactory results and accuracy improvements for the other LCU classes, with 85% or better accuracy in the three study regions, compared to other datasets. These findings indicate the possible effectiveness of the proposed multi-index set as a dimension reduction method in multi-temporal analysis. Further studies are planned to integrate multi-temporal and multi-polarization SAR data, such as Sentinel 1 (to process chain), in order to take advantage of the SAR data in the separation of bare lands and built-up areas. **Author Contributions:** Formal analysis, P.E.O. and U.A. ; Methodology, S.K. and E.S. ; Project administration, S.K. and E.S. ; Validation, P.E.O., E.S. and U.A. ; Visualization, P.E.O. and U.A. ; Writing--original draft, P.E.O., S.K., E.S., and U.A.