## 4 Results and Discussion
### SAR Data
Figure 9: Eight texture features (**a**–**h**) obtained from the ALOS-2 HH polarization with a window size of 13 \(\times\) 13, angle of 0\({}^{\circ}\), and displacement of 1 pixel. The window size is an important parameter for the texture measures. In this study, window sizes ranging from 3 \(\times\) 3 to 21 \(\times\) 21 were all evaluated to estimate the most suitable window size. Figure 11 shows the kappa coefficients for the classification using only the backscatter values of ALOS-2 (HH, HV) and Sentinel-1 (VV, VH), and those by the layer stacking of the backscatter values and their PCTs results for different window sizes. The numbers of pixels in each land cover class in the truth data are not equal, thus the kappa coefficient is plotted in Figure 11. The solid lines represent the results from SVM classification and the dashed lines those from ML classification, both for ALOS-2 and Sentinel-1. The graph shows an increase of the kappa coefficient by introducing the texture measures for the classification. The performance of SVM is better than that of ML classification. For the ALOS-2 satellite, the difference between the both methods increases significantly when the texture window size increases. The highest kappa coefficient was observed in the classification for ALOS-2 with window size 13 by SVM as shown by green arrow in the graph. The highest kappa coefficient of Sentinel-1 classification results was obtained from SVM in window size 11 \(\times\) 11. Moreover, ALOS-2 provided better performance than Sentinel-1. Figure 12 illustrates the SVM classification of Tehran using the layer stacking of backscatter values and their PCTs for the both ALOS-2 and Sentinel-1 satellites. The PCT was calculated using a window sizes of 13 \(\times\) 13 and 11 \(\times\) 11 for the ALOS-2 and Sentinel-1, respectively. Figure 11: Kappa coefficient calculated from SVM and ML classification methods using only the backscatter of ALOS-2 and Sentinel-1 and those by the layer stacking of the backscatter values and their PCTs for window sizes ranging from 3 \(\times\) 3 to 21 \(\times\) 21. Green arrows show the highest Kappa coefficient. Figure 10: The first three principal components (**a–c**) prepared from the eight texture features of the ALOS-2 HH polarization. Herein, further evaluation is performed only for the datasets that produced the highest kappa coefficient. Thus, for the sake of brevity, the classification result from the ALOS-2 backscatter values and their first three PCTs using a window size of 13 \(\times\) 13 is referred as ALOS-2-PCT. Similarly, the classification result from the Sentinel-1 backscatter values and their first three PCTs using a window size of 11 \(\times\) 11 is referred as Sentinel-1-PCT. Tables 4-6 illustrate the confusion matrix for the Landsat-8 image, ALOS-2 (backscatter values only), and ALOS-2-PCT, respectively. Tables 7 and 8 represent the confusion matrix for the Sentinel-1, and the Sentinel-1 PCT, respectively. The tables show a remarkable improvement in both overall accuracy and kappa coefficient for the datasets that include texture measures over the datasets of only backscattering values and Landsat 8. The Landsat 8 produced an overall accuracy and kappa coefficient of 41.1% and 0.25, respectively. Tables 5 and 6 depict that the overall accuracy and kappa coefficient of the ALOS-2-PCT is greater than the ALOS-2. The overall accuracy increased from 53.0% to 69.7% and the kappa coefficient from 0.38 to 0.58 when the texture measures of window size 13 \(\times\) 13 were included. Moreover, the Sentinel-1 PCT shows higher overall accuracy and kappa coefficient than the Sentinel-1. Tables 7 and 8 show an increase from 45.7% to 54.2% for the overall accuracy and an increment from 0.29 to 0.41 for the kappa coefficient when the textures of window size 11 \(\times\) 11 were included. The confusion matrix includes the producer and user accuracies as well. The diagonal elements in these tables depict the correctly classified pixels in each land cover class.