## 5 Results  
### Segmentation Quality Assessment  
Multiresolution segmentation parameters are determined experimentally. There are some parameters in the literature to evaluate segmentation quality. The most used parameters are segmentation anomalies known as undersegmentation (US) [55] and oversegmentation (OS) [55] (Equations (14) and (15)). The division of the real object into more than one small subsegment is called oversegmentation, while part of the real object becoming part of another object is called undersegmentation [56]. These values are calculated as a function of the reference object area (A\({}_{\text{r}}\)) and the object area obtained as a result of segmentation (A\({}_{\text{s}}\)). In this study, US, OS, root mean square (RMS) [55], area fit index (AFI) [57], and quality rate (QR) [58] were used as segmentation quality metrics (Equations (16)-(18)). In good segmentation, US, OS, RMS, and AFI are expected to be 0 and the QR value is expected to be 1. Quality metrics were calculated for each test image. Some subsets of the test image are shown in Figures 10 and 11. \[Over-segmentation\ (OS)\ =\ 1-\frac{A_{r(i)}\cap A_{s(i)}}{A_{r(i)}} \tag{14}\]  
\[Under-segmentation\ (US)\ =\ 1-\frac{A_{r(i)}\cap A_{s(i)}}{A_{s(i)}} \tag{15}\]  
\[Root\ Mean\ Square\ (RMS)\ =\ \sqrt{\frac{OS^{2}-US^{2}}{2}} \tag{16}\]  
\[Area\ fit\ index\ (AFI)\ =\ \frac{A_{r(i)}-A_{s(f)}}{A_{r(i)}} \tag{17}\]  
\[Quality\ rate(QR)\ =\ \frac{A_{r(i)}\cap A_{s(i)}}{A_{r(i)}\cup A_{s(i)}} \tag{18}\]  
According to segmentation quality metrics, the Sentinel dataset has better segmentation quality than the Zurich Summer dataset. The most important reason for this is the difference in resolution and the number of classes. As the resolution and the number of classes decrease, the segmentation quality increases. Because high-resolution images  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**U-Net Model**} & \multicolumn{6}{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** & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} & \multicolumn{2}{c}{**F1\%**} & \multicolumn{2}{c}{**IoU\%**} \\ \hline AR & 75.57 & 86.39 & 90.45 & 90.63 & 71.99 & 84.67 & 94.73 & 95.00 & 95.22 & 96.02 & 90.46 & 92.52 \\ FSN & 78.03 & 66.58 & 86.19 & 77.41 & \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 & 93.98 & 92.36 & 97.42 & 97.14 & & 96.70 & & & & \\ Av. & 81.34 & 66.01 & 74.04 & 90.74 & & 86.67 & & & & & \\ Kappa & 91.77 & 90.23 & 87.62 & 95.81 & & 95.35 & & & & \\ \hline \hline \end{tabular}
\end{table}