## 3 Results  
### Performance of the Different Extraction Methods under the Double-Exposure Algorithm  
In this study, linear regression analysis was conducted of the FVC results obtained from 414 sets of normal-exposure images with shadows and five types of double-exposure fusion images using five vegetation extraction indices. The results are shown in Figures 5-9. The diagonal line in the figures is the reference line indicating equal predicted and true FVC values. Each index responds differently to the different double-exposure images. Among them, the CIVE and EXG indices performed the best on the nor+over2 image. The HUE achieved the highest accuracy in extracting the FVC from the nor+over3 image. The EXR and EXGR indices achieved the best vegetation extraction results on the nor+over4 and nor+over5 images, respectively. The fusion of normal-exposure images with images compensated at the +1 ev gear, resulting in the nor+over1 image, was insufficient for effectively solving the problem of low accuracy of FVC under shadow conditions. This occurs because the lower exposure compensation level fails to completely avoid the presence of shadow pixels, making it challenging to capture sufficient pixel information under shadows. As a result, the obtained double-exposure image pixels are still damaged by shadows. Therefore, to fully leverage the benefits of the double-exposure algorithm, it is crucial to choose appropriate exposure compensation and utilize vegetation indices based on actual shadow conditions. This approach can significantly improve the FVC accuracy from shadow images and mitigate the adverse effect of shadows on vegetation extraction. In Figure 5a, Figure 6a, and Figure 7a, the scatter points derived from the CIVE, EXG, and HUE indices under normal exposure conditions are mostly clustered around or below the diagonal line. This indicates that for some experimental datasets under normal exposure, the predicted FVC is lower than the true FVC. In other words, some shaded vegetation in the images was not correctly identified. As shown in Figures 8a and 9a, the scatter points for the EXR and EXGR indices under normal exposure conditions are more scattered and disorganized. These two methods indicate cases where the extracted FVC is higher than the true FVC, as well as instances where it is lower. These findings suggest that these indices not only fail to detect vegetation in shadow areas but also cause misclassification of non-vegetation areas as vegetation areas. Among the five indices, using double-exposure images generally improved the FVC accuracy over using normal-exposure images as a reference. However, while the double-exposure method significantly reduced vegetation misdetection or misclassification, only the HUE index achieved the best performance in terms of all three metrics: R\({}^{2}\), bias, and RMSE. The predicted FVC exhibited a strong correlation with the true FVC, demonstrating reliability and stability. Particularly in the nor+over3 image, the R\({}^{2}\), bias, and RMSE values were increased from 0.750, \(-\)0.018, and 0.146 in the normal-exposure image to 0.969, 0.006, and 0.046, respectively, indicating excellent vegetation extraction capability. To provide a comprehensive and objective evaluation of the experimental results for the different double-exposure images, the results were also analyzed from a semantic segmentation perspective. This analysis employed evaluation metrics such as the mIOU index and kappa coefficient. Tables 1-5 provide the specific performance of the five indices given the normal- and double-exposure data. A comparison between the double- and normal-exposure data indicated that the use of double-exposure data significantly improved the FVC accuracy among the five indices. For example, the notable increase in recall values for the EXG and CIVE indices indicates a significant reduction in the number of missed detections of vegetation. The significant increase in precision for the EXG index suggests a lower probability of misclassifying non-vegetation as vegetation.