# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 4 Experiments
### Experimental Results  
#### 4.5.1 Classic Network Comparison Experiment  
The quantitative evaluation results are given in Table 3. It shows that the segmentation accuracy of U-Net++ and RAU-Net++ is higher than the other seven models. Among the nine models, RAU-Net++ obtains the highest IOU. The prediction accuracy is significantly improved by replacing the backbone network as well as improving the dense hopping connections compared to U-Net++; RAU-Net++ obtains 99.78%, 99.39%, 99.71%, and 99.75% for Precision, IOU, Recall, and F1 score, respectively. Since a larger value of ED or a smaller value of ED\({}^{\prime}\) is better, both ED and ED\({}^{\prime}\) of RAU-Net++ are optimal with values of 1.411 and 0.003, respectively. Compared with the other eight methods, the IOU increased by 30.7%, 16.06%, 14.4%, 13.71%, 5.82%, 3.67%, 2.81% and 0.55%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** & **ED** & **ED\({}^{\prime}\)** \\ \hline PSPNet & 80.39 & 68.69 & 93.81 & 86.55 & 1.235 & 0.206 \\ Deeplabv3+ & 89.41 & 83.33 & 97.14 & 93.11 & 1.320 & 0.110 \\ UperNet & 90.14 & 84.99 & 97.62 & 93.73 & 1.329 & 0.101 \\ MANet & 91.77 & 85.68 & 96.69 & 94.17 & 1.333 & 0.089 \\ TransUNet & 96.33 & 93.57 & 98.49 & 97.40 & 1.378 & 0.040 \\ LinkNet & 97.33 & 95.72 & 99.02 & 98.17 & 1.388 & 0.028 \\ U-Net & 97.90 & 96.58 & 99.13 & 98.51 & 1.393 & 0.023 \\ U-Net++ & 99.41 & 98.84 & 99.60 & 99.51 & 1.407 & 0.007 \\ RAU-Net++ & 99.78 & 99.39 & 99.71 & 99.75 & 1.411 & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results on the dataset of the mainstream of the Tarim River.  
In order to compare the river channel extraction effect of various network models more intuitively, five remote sensing river images of 512 \(\times\) 512 size are selected from the test data for demonstration and evaluation in this paper. Figure 9 shows the river channel extraction results of different networks. From top to bottom, the original remote sensing images, the original ground truth, and the PSPNet, Deeplabv3+, UperNet, MANet, TransUNet, LinkNet, U-Net, U-Net++, and RAU-Net++ are presented for the Prediction results. We use the image river dataset as the input image, and from the segmentation results of PSPNet, Deeplabv3+, UperNet, TransUNet, and LinkNet, the edges of the river cannot be segmented accurately, and there are many details missing. For instance, numerous river branches in designated area B cannot be finely segmented, leading to blurred and inaccurate segmentation edges. From the segmentation of U-Net, U-Net++, and RAU-Net++, the fineness of segmented river edges is significantly higher than that of the above five classical networks. Moreover, since U-Net uses ordinary jump connections for fusing multi-scale features, it leads to insufficient fusion of feature maps between the encoder and decoder, which makes the segmentation effect worse than that of U-Net++, which has dense jump connections. In addition, many mislabeled pixels were produced; for example, it is clear from the segmentation at C and F of MANet that a non-river branch was mis-detected as a river. Due to the optimized complementation of dense jump connections by the inclusion of the residual attention feature fusion module in RAU-Net++, RAU-Net++ is able to detect the regions that U-Net++ mis-detects and misses; for example, RAU-Net++ corrects the mis-detection as well as the omission of a river by U-Net++ in a small area at the marked regions A, B, D, and E. In Figure 11, by comparing the nine methods on five river remote sensing images, it can be seen that RAU-Net++ segmented the edges of the river channel finely and accurately, and retained satisfactory detailed features of the river channel.  
Figure 11: Cont.  
Figure 11: _Cont._