# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 5 Experimental Evaluation and Discussion
### Discussion and Future Work
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**UNet**} & \multicolumn{4}{c}{**PSPNet**} & \multicolumn{4}{c}{**HRNet**} \\ \cline{2-13} \multicolumn{1}{c}{**Classes**} & \multicolumn{2}{c}{**Images with Noise**} & \multicolumn{2}{c}{**Images with Noise Free**} & \multicolumn{2}{c}{**Images with Noise Free**} & \multicolumn{2}{c}{**Images with Noise**} & \multicolumn{2}{c}{**Images with Noise Free**} \\ \cline{2-13} \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} \\ \cline{2-13} \multicolumn{1}{c}{**Classes**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} \\ \hline Urban Areas & 12.29 & 25.21 & 18.06 & 34.84 & 14.86 & 27.91 & 15.20 & 32.06 & 21.13 & 39.77 & 27.13 & 44.27 & \\ \hline Agricultural Areas & 57.57 & 65.12 & 58.64 & 67.74 & 62.41 & 68.94 & 64.32 & 71.23 & 71.54 & 83.24 & 74.47 & 85.2 \\ \hline Forest Areas & 69.09 & 80.34 & 71.35 & 83.46 & 73.87 & 84.04 & 77.43 & 87.92 & 72.33 & 82.74 & 74.06 & 84.71 \\ \hline Peatland, Bogs and Marshes & 70.67 & 83.78 & 72.17 & 85.42 & 74.45 & 86.63 & 78.36 & 89.53 & 74.49 & 86.21 & 79.59 & 89.67 \\ \hline Water & 92.67 & 94.15 & 94.15 & 95.58 & 92.11 & 94.21 & 94.66 & 96.90 & 93.51 & 95.56 & 97.78 & 98.49 \\ \hline Summary (Average) & 60.46 & 69.72 & 62.87 & 73.41 & 63.54 & 72.34 & 65.99 & 75.53 & 66.6 & 77.50 & 70.60 & 80.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the segmentation MA and mIoU values of various deep neural networks. reduce the noise in the images. Then, Sentinel-2, Sentinel-1, spectral indices, and ALOS elevation data were evaluated with various band combinations. As a result, with modified U-TAE, a mIoU of 57.25%; with random forest, a mIoU of 39.69%; with U-Net, a mIoU of 55.73%; and with SegFormer, an mIoU of 53.5% is reached. We focused primarily on auto-labeled noise-free dataset creation, then, by using these enhanced noise-free datasets, attempted to improve accuracy in this field with state-of-the-art deep learning models. We applied three state-of-the-art CNN models to try our new dataset. Additionally, after applying the models, we evaluated the performance of the models and proved which model provided the best accuracy. Confirmation of the model with the highest success with the McNemar test increases the reliability of our proposal. For future work, it would be interesting to develop deep learning models specially for SAR segmentation and it would also be interesting to achieve more success for urban areas segmentation; another type of CNN model or RNN model could also be examined. Additionally, our pipeline could be evaluated by using another dataset for ground-truth instead of CORINE.