# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 3 Results and Discussion
### Experiments in Complex Scenes  
Besides the simple river scenes, there are also some complex outdoor scenes where the traditional algorithms are difficult to take effect or even fail. Tests of different methods on complex scenes were conducted, among which four typical examples are shown in Figure 12 with the corresponding criteria values shown in Table 2.  
Moreover, Figure 13 shows the speed of different methods in complex river scenes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**River Scene**} & \multicolumn{3}{c}{**Intensity + Texture**} & \multicolumn{3}{c}{**Edge Detection**} & \multicolumn{3}{c}{**Our Algorithm**} \\ \cline{2-10}  & **PA** & **MIoU** & \(F_{\beta}\) & **PA** & **MIoU** & \(F_{\beta}\) & **PA** & **MIoU** & \(F_{\beta}\) \\ \hline (a) & 0.908 & 0.708 & **0.759** & 0.987 & 0.629 & **0.708** & 0.908 & 0.878 & **0.887** \\ (b) & 0.864 & 0.822 & **0.834** & 0.999 & 0.722 & **0.789** & 0.948 & 0.910 & **0.921** \\ (c) & 0.657 & 0.664 & **0.662** & 0.995 & 0.845 & **0.886** & 0.913 & 0.873 & **0.885** \\ (d) & 0.854 & 0.775 & **0.798** & 0.981 & 0.830 & **0.871** & 0.934 & 0.931 & **0.932** \\ Average of 390 images & 0.802 & 0.745 & **0.762** & 0.985 & 0.745 & **0.805** & 0.935 & 0.925 & **0.929** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of different methods in complex scenes.  
Figure 11: Speed of different methods in simple scenes.  
Figure 12: Segmentation results of different algorithms in complex scenes (**a–d**). From left to right: The first column are the input images after pre-processing; The second and third column are respectively the segmentation results using “intensity + texture” features and the adaptive threshold edge detection algorithm; The fourth column are the results of our method.  
Figure 13: Speed of different methods in the complex scenes.  
The proposed algorithm showed robust performance in complex scenes, and it got the highest \(F_{\beta}\) and the least time cost compared with other methods. The proposed algorithm is proved effective and has better segmentation performance of river images.  
The method utilizing \"intensity + texture\" features was prone to false detection. As shown in Figure 12, some pixels on the riverside were also detected as water. This is because some parts of the riverside in the image had similar features to the designed one. Therefore, the method simply using global image features could be confused. As for the method based on edge detection, it was likely to miss part of the water region with shadow due to the strong edge of clear shadow. This method could not distinguish whether the detected edge was a riverbank or other edges, which resulted in mistakes. However, the improved LBP and \(H\) variance features designed in this study were local features based on the water surface reflection mechanism, which was close to characteristics of water pixels. Such features could describe not only common water part of the image, but also those with complex appearance like light and covered shadow. To illustrate this, the results of each step in our algorithm are shown in Figure 14 to show how it works.  
Figure 14: Performance of the proposed method in complex scenes (**a–d**). From left to right, the first column includes the images after pre-processing. In the second column,The blue squares in images represent the detection result of water region with the improved LBP feature. In the third column the green squares represent the detection result by \(H\)-variance feature developed from the second column images. The fourth column images are the binary mask of detection results after the designed morphological operation. The fifth column images are final segmentation results of river region indicated by a covered translucent blue area.