# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 5 Results and Discussion
### Validation on Independent Test Regions  
The efficiency and applicability of the proposed approach was tested in two metropolitan cities of Turkey, Ankara, and Konya. These regions were good candidates for evaluating the performance of the multi-index method because they include residential and industrial areas surrounded by extensive bare land, which suited the main objective of this research. Additionally, the image acquisition dates represented different seasonal conditions. The SVM classification was performed on these regions for the three datasets, which were the original ten-band Sentinel-2A image, the NDBI, NDVIre, MNDWI dataset, and the NDTI, NDVIre, MNDWI dataset. The training sample distribution and the number accuracy assessment points are presented in Table 3.  
The classification results of the Ankara region are presented in Figure 11 and the accuracy assessment values are provided in Tables 6 and 7. The results showed that the accuracy improvement using the NDTI-based multi-index image was evident. This combination provided higher accuracies for bare land, industry, and built-up classes, indicating a better separation among them. Additionally, the consistency between the producer's and the user's accuracy was higher than that of the other datasets. The overall accuracy was improved by, approximately, 30-40%. The two sub-regions presented in Figure 11 shows that the classification result of the ten-band multispectral image (Regions 1b and 2b) was dominated by the built-up and the industrial lands, and it misclassified the bare land surrounding them, especially in low-density urban areas. There were incorrectly classified regions near the green areas, due to this effect, which occurred in the north-west and east of both sub-regions. The NDBI-based, multi-index set was less affected by this over classification, but it suffered while trying to distinguish between industry and built-up classes and false classification occurred due to labelling an extensive amount of the built-up areas as industrial land (Regions 1c and 2c). The classification results of the Konya region are presented in Figure 12 and the accuracy assessment values are provided in Tables 8 and 9. The accuracy improvement and consistency between the producer's and the user's accuracy with the NDTI-based dataset was similar to that in the Ankara region, and the other datasets suffered from low accuracies for the bare land, built-up, and the industry classes. The evaluation in Figure 12 shows that the ten-band multispectral imagery classification over-classified built-up lands and could not detect the industrial areas, which could be seen clearly in Region 2b. For the NDBI-based dataset, the bare land in the western part of Regions 1 and 2 were over-classified as built-up land (Regions 1c and 2c). Moreover, there were improvements in the accuracy metrics of the asphalt, forest, and vegetation classes, when applying the proposed method (Regions 1d and 2d). Figure 11: **(a)** RGB image of Ankara from Sentinel-2A, **(b)** the classified Sentinel-2A, **(c)** the classified multi-index (NDBI, NDVIire, and MNDWI) and **(d)** the classified multi-index (NDTI, NDVIire, and MNDWI) images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(Sentinel-2A Image (10 bands))** & **(NDBI+NDVIre+MNDWI)** & **(NDTI+NDVIre+MNDWD)** \\ \hline
**Land Cover** & **Producer’s** & **User’s** & **Producer’s** & **User’s** & **Producer’s** & **User’s** \\
**Class** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline Bare land & 59.52 & 22.73 & 43.18 & 34.55 & 89.77 & 90.80 \\ Asphalt & 97.50 & 66.10 & 92.50 & 84.09 & 100.00 & 97.56 \\ Water & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Industry & 26.67 & 53.33 & 70.00 & 100.00 & 83.33 & 100.00 \\ Built-Up & 84.34 & 43.48 & 20.48 & 48.57 & 92.77 & 92.77 \\ Forest & 94.44 & 65.39 & 88.89 & 72.73 & 100.00 & 81.82 \\ Veg. Cover & 36.36 & 100.00 & 93.18 & 57.75 & 97.72 & 95.56 \\ \hline \hline \end{tabular}
\end{table}