## 3 Results
### Performance of the Different Extraction Methods under the Double-Exposure Algorithm
Combining Figure 7 and Table 3, it can be concluded that when using double-exposure data for vegetation extraction, the HUE index provides a significant improvement in accuracy relative to normal-exposure data. This indicates that the HUE index is well-suited for analyzing double-exposure data. Additionally, the FVC accuracy under the HUE index is substantially higher than it is under the other four indices. Among all five indices evaluated, the HUE index is the optimal choice for calculating FVC when using double-exposure data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **nor** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** \\ \hline
**mIOU** & 0.649 & 0.716 & 0.752 & 0.786 & **0.802** & 0.793 \\
**IOU** & 0.708 & 0.756 & 0.784 & 0.815 & **0.825** & 0.815 \\
**Accuracy** & 0.788 & 0.833 & 0.859 & 0.885 & **0.898** & 0.893 \\
**Precision** & 0.797 & 0.848 & 0.874 & 0.899 & **0.904** & 0.899 \\
**Recall** & 0.870 & 0.880 & 0.890 & 0.903 & **0.910** & 0.905 \\
**Kappa** & 0.533 & 0.636 & 0.690 & 0.738 & **0.760** & 0.752 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy statistics of vegetation classification for five double-exposure fusion combinations of the double-exposure algorithm and normal-exposure images using the EXR. Figure 8: Scatter plots of FVC estimated from normal-exposure images and five double-exposure combinations using the double-exposure algorithm based on the EXR compared to the true FVC. (**(a)** scatter plot of the FVC obtained from the normal-exposure images compared to the true FVC; **(b)** scatter plot of the FVC obtained from the nor+over1 double-exposure fusion image compared to the true FVC; **(c)** scatter plot of the FVC obtained from the nor+over2 double-exposure fusion image compared to the true FVC; **(d)** scatter plot of the FVC obtained from the nor+over3 double-exposure fusion image compared to the true FVC; **(e)** scatter plot of the FVC obtained from the nor+over4 double-exposure fusion image compared to the true FVC; **(f)** scatter plot of the FVC obtained from the nor+over5 double-exposure fusion image compared to the true FVC). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **nor** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** \\ \hline
**mIOU** & 0.815 & 0.872 & 0.886 & 0.892 & **0.895** & 0.888 \\
**IOU** & 0.837 & 0.894 & 0.906 & 0.910 & **0.912** & 0.906 \\
**Accuracy** & 0.894 & 0.937 & 0.945 & 0.947 & **0.949** & 0.947 \\
**Precision** & 0.945 & 0.964 & 0.968 & 0.969 & 0.972 & **0.974** \\
**Recall** & 0.887 & 0.927 & 0.936 & **0.938** & **0.938** & 0.930 \\
**Kappa** & 0.780 & 0.852 & 0.869 & 0.875 & **0.879** & 0.870 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy statistics of vegetation classification for five double-exposure fusion combinations of the double-exposure algorithm using the EXGR. Figure 10 shows a set of experimental data with a high proportion of shadows, in which shadows blur pixel information and have a greater impact on the accuracy of FVC. After merging overexposed images, the distribution of vegetation and non-vegetation becomes more clearly visible. Moreover, double-exposure images with different degrees of overexposure show varying results, as demonstrated in the first column. The change in pixel areas within the red circle is particularly noticeable. Consequently, the vegetation extraction results obtained from the shadow-affected normal-exposure image and the shadow-removed double-exposure fusion image using the five vegetation indices substantially differ as a result. When extracting vegetation from the normal-exposure data, the results across the various vegetation indices indicate that a high proportion of shadow nearly renders the provided vegetation extraction function ineffective. Among these, the CIVE and EXG perform better compared to other indices.