# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 5 Conclusions  
Building on the approach to finely segment the dynamic edges of perennially changing river channels and accurately extract information from variable fine river branches, we introduce RAU-Net++, a river channel extraction model for remote sensing imagery. Due to the false detection and missed detection of U-Net++ in small river channels, our model leverages VGG16 for river feature extraction and enhances expressiveness by integrating CBAM at the backbone network's end and our custom RAFF module at dense skip connections. The application of a weighted loss function effectively reduces training errors. Finally, ablation experiments on two datasets confirm the overall effectiveness and generalization of the network. Comparative analyses demonstrate the model's optimal performance, affirming its efficacy and superiority in enhancing segmentation. Furthermore, comprehensive validation through diverse network prediction graphs reinforces the model's reliability. In prospect, notwithstanding noteworthy strides in the field, persistent challenges endure in the domain of remote sensing river segmentation. At present, the ground resolution of satellite images is constantly improving, and in the future, research and analysis of higher-resolution remote sensing river images are needed. Secondly, the substantial temporal commitment entailed by manual annotation could be mitigated through the adoption of weakly supervised learning methodologies. This strategy entails harnessing annotated data to acquire unlabeled counterparts, thereby ameliorating the temporal and resource burdens associated with manual labeling. Additionally, owing to the network's depth and parameter intricacies, computational demands mandate high-performance graphics processing units (GPUs). In ablation experiments, the model computation, complexity, and real-time performance for RAU-Net++ are shown in Table 12. Consequently, forthcoming initiatives will be directed towards the judicious optimization of model parameters, aiming to streamline computational processes and augment training efficiency without perceptibly compromising the precision of the network. **Author Contributions:** Conceptualization, Y.T. and J.Z. ; Methodology, Y.T. ; Software, Y.T. ; Validation, Y.T., J.Z. and Z.J. ; Formal analysis, Y.T. ; Survey, Y.T. ; Resources, Y.T. ; Data management, Y.T. ; First draft, Y.T. ; Writing--comments and editing, Y.T. ; Visualization, Y.T. and Y.L. ; Supervision, J.Z. and Z.J. ; Project Management, J.Z. ; Financing acquisition, Y.T., J.Z., Z.J., Y.L. and P.H. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** & **ED** & **ED\({}^{\prime}\)** \\ \hline U-Net & 96.13 & 93.24 & 97.78 & 96.94 & 1.371 & 0.0445 \\ U-Net plusplus & 96.67 & 93.95 & 97.89 & 97.27 & 1.376 & 0.0393 \\ U-Net plusplus + Vgg16 & 96.56 & 93.96 & 97.99 & 97.27 & 1.376 & 0.0396 \\ U-Net plusplus + Vgg16 + CBAM & 96.63 & 93.74 & 97.98 & 97.30 & 1.376 & 0.0392 \\ U-Net plusplus + Vgg16 + RAFF & 96.64 & 93.98 & **98.00** & 97.31 & 1.376 & 0.0390 \\ U-Net plusplus + Vgg16 + CBAM + RAFF & 97.18 & 94.00 & 97.69 & 97.34 & 1.377 & 0.0376 \\ U-Net plusplus + Vgg16 + CBAM & **98.21** & **94.02** & 96.72 & **97.46** & **1.378** & **0.0374** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: NWPU-RESISC45 dataset overall network ablation experiments results. The bolded section indicates optimal parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Params (M)** & **GFLOPs** & **FPS (s)** \\ \hline U-Net & 18.439 & 154.524 G & 817 \\ U-Net plusplus & 29.317 & 703.506 G & 737 \\ U-Net plusplus + Vgg16 & 34.627 & 746.993 G & 692 \\ U-Net plusplus + Vgg16 + CBAM & 34.635 & 747.005 G & 583 \\ U-Net plusplus + Vgg16 + RAFF & 68.206 & 1.913 T & 293 \\ U-Net plusplus + Vgg16 + CBAM + RAFF + Weight Loss & 68.214 & 1.913 T & 282 \\ \hline \hline \end{tabular}
\end{table}