## 5 Results
### Segmentation Quality Assessment

Table 12: Kocaeli test site with 7 bands: performance metrics with U-Net. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**U-Net Model**} & \multicolumn{8}{c}{**CNN (U-Net)–MRS**} \\ \cline{2-13}
**Land** & \multicolumn{2}{c}{**32 \(\times\) 32**} & \multicolumn{2}{c}{**64 \(\times\) 64**} & \multicolumn{2}{c}{**128 \(\times\) 128**} & \multicolumn{2}{c}{**32 \(\times\) 32**} & \multicolumn{2}{c}{**64 \(\times\) 64**} & \multicolumn{2}{c}{**128 \(\times\) 128**} \\ \cline{2-13}
**Cover** & **F\({}_{1}\) \%** & **IoU\%** & **F\({}_{1}\)\%** & **IoU\%** & **F\({}_{1}\)\%** & **IoU\%** & **F\({}_{1}\)\%** & **IoU\%** & **F\({}_{1}\)\%** & **IoU\%** & **F\({}_{1}\)\%** & **IoU\%** \\ \hline AR & 82.74 & 84.99 & 70.06 & 82.92 & 66.71 & 76.76 & 88.64 & 91.89 & 85.27 & 91.30 & 89.76 & 89.39 \\ FSN & 74.80 & 85.85 & 68.62 & 83.17 & 62.74 & 71.41 & 89.72 & 93.31 & 81.69 & 91.34 & 76.51 & 85.60 \\ WB & 86.48 & 95.53 & 59.34 & 94.55 & 92.66 & 98.13 & 93.85 & 97.49 & 93.04 & 97.56 & 98.38 & 99.44 \\ \hline OA & 94.92 According to the five-band dataset results for the Istanbul city test site, the highest overall accuracy value was obtained with the CNN-MRS model with U-Net model patch size of 64 \(\times\) 64 as 97.31% (Table 6). The lowest overall accuracy for the Istanbul test site was obtained with the SegNet model with a patch size of 128 \(\times\) 128 as 79.41% (Table 7). The best performance for individual class F1-score was obtained with the CNN (U-Net)-MRS model with 64 \(\times\) 64 patch size in the experiment for the water bodies class as 98.43% (Table 6). The worst F1-score was obtained by the SegNet model patch size 128 \(\times\) 128 for forest and semi-natural class as 51.36% (Table 7). The best per-class score of IoU was found for the CNN (U-Net)-MRS model with 32 \(\times\) 32 patch size for waterbodies class as 99.51% (Table 6). The lowest value of IoU was found for the SegNet model with 128 \(\times\) 128 patch size for forest and semi-natural areas class as 53.67% (Table 7). Generally, artificial surface performance values for the models are at a modest level. It is also discernible in the semantic segmentation results that all CNN-MRS model results are better than the state-of-the-art CNN-based models. In the Istanbul test site area, it is noticeable for the seven-band dataset that the best performance was obtained with the CNN (SegNet)-MRS model with 128 \(\times\) 182 patch size as overall accuracy of 97.09% and kappa of 94.40%, and the CNN (U-Net)-MRS model with 32 \(\times\) 32 patch size had an average F1-score of 94.68% (Tables 8 and 9). Again, the same model yielded the best scores for artificial surfaces and forest and semi-natural areas classes as class metrics. The best performing model for the water bodies class is the same model with the 128 \(\times\) 128 patch size. The worst performance results were found for the SegNet model with 32 \(\times\) 32 patch size for overall accuracy at 89.23%, average F1-score at 71.74%, and kappa coefficient at 80.12% (Table 9). The classes were predicted better with the CNN-MRS model than with state-of-the-art CNN models. Notably, for the U-Net model with 128 \(\times\) 128 patch size, the result has a strong salt-pepper effect (Figure 12). The CNN-MRS model used for this model helped to improve the results greatly. The bridge over the Bosphorus as an artificial surface class has disappeared in the results of some models. The bridge was segmented by CNN (U-Net)-MRS model with 32 \(\times\) 32 and 64 \(\times\) 64 patch sizes and CNN (SegNet)-MRS model with 64 \(\times\) 64 patch size. Land cover classification results of five- and seven-band datasets for the Istanbul test site area are shown in Figures 12 and 13, respectively. The other test area was selected in different cities from the training areas. Thus, the usability of the trained models in different test areas was investigated. The Kocaeli test area results were comparable to those of the Istanbul test site.