## 4 Discussion  
### Advantages of Applying the Double-Exposure Algorithm  
The double-exposure algorithm proposed in this paper addresses the issue of low FVC accuracy in the case of shadows. By merging normal-exposure and overexposed images, this approach efficiently captures the pixel information in shadow areas to restore their true characteristics, facilitating accurate vegetation identification. Extracting shadow-affected vegetation essentially involves capturing real-life scenarios under an extreme lighting contrast. For this purpose, scholars worldwide have proposed several approaches. The main solutions are as follows: (1) The probability statistics method can be used. One approach to extracting vegetation from digital images entails using the segmentation point between vegetation and non-vegetation in the a* component of the LAB color space [49]. The channels in this color space exhibit a low correlation and have been utilized in studies focused on mitigating the impact of vegetation shadows [50; 51]. [PERSON] et al. [47] suggested preprocessing the brightness histogram of the digital image in the HIS color space, followed by utilizing the probability distribution of the a* component for FVC calculation. This method's performance evaluation relies solely on the overall accuracy of FVC calculation from the image. It does not require or consider assessing the classification correctness \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **nor** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** \\ \hline \multirow{4}{*}{0–40\% shadow pixels} & **mIOU** & 0.932 & 0.940 & **0.941** & 0.940 & 0.938 & 0.938 \\  & **IOU** & 0.936 & **0.954** & **0.954** & 0.953 & 0.953 & 0.953 \\  & **Accuracy** & 0.973 & **0.979** & **0.979** & **0.979** & 0.978 \\  & **Precision** & 0.960 & 0.981 & **0.982** & **0.982** & 0.981 & **0.982** \\  & **Recall** & 0.950 & **0.971** & **0.971** & **0.971** & 0.970 & 0.970 \\  & **Kappa** & 0.920 & 0.936 & **0.937** & 0.936 & 0.934 & 0.934 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of vegetation extraction performance under weak shadow conditions (0–40% shadow pixels) using the HUE index-based double-exposure algorithm for five double-exposure fusion combinations. The higher the accuracy indicator value, the higher the vegetation extraction accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **nor** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** \\ \hline \multirow{4}{*}{40–70\% shadow pixels} & **mIOU** & 0.811 & 0.892 & 0.909 & **0.918** & 0.913 & 0.914 \\  & **IOU** & 0.793 & 0.901 & 0.923 & **0.937** & 0.931 & 0.931 \\  & **Accuracy** & 0.883 & 0.953 & 0.961 & **0.966** & **0.966** & **0.966** \\  & **Precision** & 0.810 & 0.927 & 0.958 & **0.973** & 0.966 & 0.967 \\  & **Recall** & 0.811 & 0.922 & 0.942 & **0.963** & 0.953 & 0.952 \\  & **Kappa** & 0.756 & 0.865 & 0.893 & **0.908** & 0.899 & 0.900 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative analysis of vegetation extraction performance under moderate shadow conditions (40–70% shadow pixels) using the HUE index-based double-exposure algorithm for five double-exposure fusion combinations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **nor** & **nor+over1** & **nor+over2** & **nor+over3** & **nor+over4** & **nor+over5** \\ \hline \multirow{4}{*}{70–100\% shadow pixels} & **mIOU** & 0.755 & 0.823 & 0.891 & **0.914** & 0.911 & 0.906 \\  & **IOU** & 0.684 & 0.785 & 0.881 & **0.918** & 0.915 & 0.910 \\  & **Accuracy** & 0.878 & 0.917 & 0.952 & **0.965** & 0.963 & 0.960 \\  & **Precision** & 0.707 & 0.819 & 0.932 & 0.973 & **0.974** & **0.974** \\  & **Recall** & 0.713 & 0.806 & 0.906 & **0.940** & 0.937 & 0.932 \\  & **Kappa** & 0.645 & 0.758 & 0.866 & **0.903** & 0.899 & 0.893 \\ \hline \hline \end{tabular}
\end{table}