## 4 Results and Discussion
### SAR Data  
Considering the limitation of the Landsat 8 image in land cover classification and detection of various built-up classes and bare land in the study area, ALOS-2 and Sentinel-1 images with the capability of obtaining ground surface information based on the backscatter coefficient were selected to overcome this issue. Figure 8 represents the color composites of the dual-polarization intensity images from the ALOS-2 and Sentinel-1 data. The cyan and red colors indicate different orientation and geometrical positions of the residential areas in Tehran. To increase the accuracy in the supervised classification from SAR data, texture analysis (GLCM) was applied to the dual-polarization images. Figure 9 depicts the texture features obtained from ALOS-2 HH polarization. Texture analysis was applied to each backscatter value of HH, HV, VV, and VH. Thus, for each polarized image, eight texture measures were obtained: mean, contrast, ASM, correlation, homogeneity, variance, entropy, and dissimilarity. Then, the PCA was applied to reduce the number of textures measured. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Land Cover Class** & \begin{tabular}{c} **Bare** \\ **Land** \\ \end{tabular} & **Vegetation** & **Built-up 1** & **Built-up 2** & **Built-up 3** & \
\begin{tabular}{c} **Overall** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Kappa** \\ **Coefficient** \\ \end{tabular} \\ \hline \multirow{2}{*}{**ML**} & **Producer Accuracy (\%)** & 30.7 & 58.7 & 31.5 & 35.0 & 33.0 & 35.3 & 0.22 \\  & **User Accuracy (\%)** & 91.2 & 81.4 & 4.0 & 47.8 & 22.0 & \\ \hline \multirow{2}{*}{**SVM**} & **Producer Accuracy (\%)** & 42.4 & 63.8 & 31.2 & 39.8 & 27.2 & 41.1 & 0.26 \\  & **User Accuracy (\%)** & 86.4 & 76.4 & 5.1 & 39.3 & 25.5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for ML and SVM classification using the Landsat 8 image. Figure 8: Color composite of dual-polarization images from the ALOS-2 image (**a**) and the Sentinel-1 image (**b**), the yellow frame shows the location of region chosen for evaluating the components of principal component analysis (PCA) and the location of validation area. In order to select the appropriate number of principal components for texture measures (PCT) to perform the classification, we examined the region shown as a yellow frame in Figure 8 using the HH and HV polarizations of ALOS-2. The SVM classification was carried out for two datasets. In the first dataset, the two backscatter values (HH and HV) and all their PCTs (18 layers in total) were used. In the second dataset, the two backscatter values and their first three PCTs (eight layers in total) were used. In both datasets the PCT was calculated from texture features obtained using a window size of 13 \(\times\) 13. The first three components contain almost 99 percent of the variation of the input elements. Table 3 shows the overall accuracy and the kappa coefficient obtained from the classification of the two datasets. A comparison shows a difference of only 1.26% for the overall accuracy and a difference of 0.02 for the kappa coefficient. Although the dataset with all the PCTs gained higher accuracy, the required time was significantly larger than that of the second dataset. Therefore, in this study, only the first three principal components were used. Figure 10 illustrates the first three PCTs of the HH polarization. The classifications using ML and SVM were performed using the same fifteen training samples shown in Figure 4. As mentioned before, the performance of both ML and SVM are compared to the truth data to observe which method produces the highest accuracy. \begin{table}
\begin{tabular}{c c c} \hline
**Datasets** & **Overall Accuracy (\%)** & **Kappa Coefficient** \\ \hline Backscatter + three PCTs (\%) & 61.79 & 0.50 \\ Backscatter + all PCTs (\%) & 63.05 & 0.52 \\ \hline \end{tabular}
\end{table} 
Table 3: Overall accuracy and kappa coefficient calculated from the SVM classification using the first three PCTs and all PCTs.