## 3 Results
### Performance of the Different Extraction Methods under the Double-Exposure Algorithm
However, they extract only a small portion of vegetation and misclassify some non-vegetation as vegetation, resulting in an increased occurrence of missed vegetation detection overall. With the introduction of double-exposure fusion data, the vegetation extraction results become more reliable. However, there are still areas where vegetation cannot be accurately extracted, and some non-vegetation areas are misidentified as vegetation areas. In the case of the HUE index, the loss of pixel information caused by shadows significantly impacts the effectiveness of vegetation extraction. However, in the nor+over2 images and higher-gear double-exposure images, the hue index demonstrates a stronger ability to extract vegetation compared to other indices, with lower probabilities of vegetation misclassification and omission. This  
Figure 9: Scatter plots of FVC estimated from normal-exposure images and five double-exposure combinations using the double-exposure algorithm based on the EXGR 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. indicates that the removal of shadows can greatly improve the HUE index's classification performance. Therefore, as the exposure compensation level increases along the positive direction, pixel information in the shadow regions of the double-exposure fusion image is increasingly restored, allowing accurate vegetation extraction. In contrast to those of the previous three indices, the extraction results of the EXG and EXGR show different results when applied to normal-exposure images with a high proportion of shadows. These two indices indicate a high frequency of vegetation misclassification in the presence of a high proportion of shadows. These two indices reveal that a significant number of non-vegetation pixels are erroneously classified as vegetation pixels, resulting in a low FVC accuracy. Although the use of double-exposure data can reduce misclassification occurrence, the overall effectiveness of vegetation extraction remains unsatisfactory, especially for results based on the EXR. Overall, in this set of experimental data, the hue index demonstrates good adaptability to double-exposure images, exhibiting superior vegetation extraction capabilities. To comprehensively analyze and evaluate the performance of the double-exposure algorithm, this study also compared it with the classic algorithm, SHAR-LABFVC. Both algorithms are based on the principle of image enhancement through intensity values. Both algorithms prove that image enhancement based on the intensity value is direct and effective. We conducted vegetation extraction on 14 sets of experimental data, with results shown in Table 6. When compared to the FVC ground truth, the double-exposure algorithm demonstrated a significant advantage in accuracy over the SHAR-LABFVC algorithm. It offers a more effective solution to the problem of vegetation extraction in shadowed  
Figure 10: Comparative illustration of normal exposure images and five types of double-exposure fusion images under shadow conditions, along with a comparison of vegetation extraction results using five vegetation indices on the images above (in all the images except those in the first column, the white areas indicate vegetation areas, while the black areas denote non-vegetation areas). The vegetation within the red circle is more strongly affected by shadows. areas.