# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Example Analysis
#### 3.3.2 Comparison of Different Model Indicators  
Table 3 shows the evaluation results of the improved U-Net in this paper and four other segmentation network models on the cloud image datasets. According to Table 3, taking the SWINySEG dataset as an example, in the comparison of accuracy metrics, the accuracy of the FCN is 0.848, followed by SegNet at 0.855, both with \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Dilate Block** & **DS Path** & **Im-CSAM** & **A** & **MIoU** & **ER** \\ \hline \hline \multicolumn{6}{c}{SWINySEG Dataset} \\ \hline Model a & & & 0.876 & 0.763 & 0.124 \\ \hline Model b & \(\surd\) & & 0.878 & 0.784 & 0.122 \\ \hline Model c & & \(\surd\) & 0.884 & 0.792 & 0.116 \\ \hline Model d & & \(\surd\) & 0.886 & 0.796 & 0.114 \\ \hline Improved U-Net & \(\surd\) & \(\surd\) & \(\surd\) & 0.895 & 0.801 & 0.105 \\ \hline \multicolumn{6}{c}{TCDD Dataset} \\ \hline Model a & & & 0.914 & 0.844 & 0.085 \\ \hline Model b & \(\surd\) & & 0.921 & 0.848 & 0.078 \\ \hline Model c & & \(\surd\) & 0.933 & 0.875 & 0.066 \\ \hline Model d & & \(\surd\) & 0.931 & 0.880 & 0.069 \\ \hline Improved U-Net & \(\surd\) & \(\surd\) & \(\surd\) & 0.943 & 0.893 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the ablation experiment on the SWINySEG and TCDD datasets. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Models** & **A** & **Pr** & **Re** & **F1-Score** & **MIoU** & **ER** \\ \hline \hline \multicolumn{6}{c}{SWINySEG Dataset} \\ \hline FCN & 0.848 & 0.863 & 0.807 & 0.834 & 0.715 & 0.152 \\ \hline U-Net & 0.876 & 0.885 & 0.847 & 0.866 & 0.763 & 0.124 \\ \hline SegNet & 0.855 & 0.890 & 0.847 & 0.868 & 0.767 & 0.145 \\ \hline CloudSegNet & 0.869 & 0.862 & 0.862 & 0.862 & 0.758 & 0.130 \\ \hline Improved U-Net & 0.895 & 0.921 & 0.850 & 0.884 & 0.801 & 0.105 \\ \hline \multicolumn{6}{c}{TCDD Dataset} \\ \hline FCN & 0.916 & 0.911 & 0.910 & 0.915 & 0.843 & 0.084 \\ \hline U-Net & 0.914 & 0.922 & 0.931 & 0.915 & 0.844 & 0.085 \\ \hline SegNet & 0.899 & 0.899 & 0.898 & 0.899 & 0.816 & 0.101 \\ \hline CloudSegNet & 0.933 & 0.921 & 0.926 & 0.920 & 0.852 & 0.067 \\ \hline Improved U-Net & 0.943 & 0.955 & 0.970 & 0.944 & 0.893 & 0.057 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of the evaluation indicators of different segmentation networks on the SWINySEG and TCDD datasets. relatively low accuracy. The proposed improved U-Net model has the highest accuracy of 0.895, with the overall segmentation accuracy being the highest. In the comparison of the metrics of precision, CloudSegNet and FCN have similar precision rates and also maintain the lowest value, U-Net and SegNet have precision rates of 0.885 and 0.890, respectively, and the proposed model in this paper has the highest precision rate of 0.921, which represents the highest level of segmentation precision among the results of positive samples. In the comparison of the metrics of recall, the FCN has the worst performance of only 0.807, U-Net and SegNet have the same performance, and the recall value of the improved U-Net in this paper is only second to CloudSegNet, with a difference of only 0.012, which is not a significant improvement, but it reaps better results. The F1-score is a comprehensive evaluation index, and the highest F1-score of 0.884 for the improved U-Net is higher than the F1-scores of the other four models, with the lowest F1-score of 0.834 for the FCN. The MIoU is a relatively important evaluation indicator in semantic segmentation tasks. The lowest MIoU value of the FCN is 0.715, followed by CloudSegNet with a MIoU value of 0.758. The performance of U-Net and SegNet is relatively close. Compared with the other four models, the improved U-Net has improved by up to 12.03%, proving that it has a larger overlapping area with the true label during segmentation prediction and has the best segmentation effect. The ER index is an extension of the accuracy index.