## 4 Discussion
### Advantages of Applying the Double-Exposure Algorithm

Table 9: Comparative analysis of vegetation extraction performance under strong shadow conditions (70–100% shadow pixels) using the HUE index-based double-exposure algorithm for five double-exposure fusion combinations. of each pixel. Therefore, the FVC accuracy using this method should be further investigated and verified. (2) HDR imaging techniques can be used. [PERSON] et al. employed the HDR imaging technique to identify shadow areas in images and mitigated their impact on vegetation extraction [38]. [PERSON] et al. proposed using HDR images to capture the vegetation distribution and obtain images with a wider dynamic range, thereby reducing the shadow occurrence frequency [41]. This technology can improve shadow issues in images, but HDR imaging cannot completely resolve shadow problems [38]. (3) Polarization information can be combined. [PERSON] et al. and [PERSON] et al. proposed the idea of capturing polarization information simultaneously while recording vegetation images to supplement the information of shadow areas in normal-exposure images, thus aiding in high-precision FVC [46; 52]. However, the acquisition of polarization information relies on the lighting conditions during photography. When the light intensity is too low, the amount of polarized light reduces, making it difficult to capture and record images, which hinders the recovery of shadow pixel information. In this study, the following factors are considered when selecting a vegetation extraction strategy under shadow conditions. First, several vegetation indices based on the RGB color space, such as CIVE, EXG, and EXGR, perform poorly under shadow conditions [53]. However, vegetation index-based methods can achieve a high classification efficiency and are easy to implement [54; 55; 56]. Second, the process of machine learning classification is complex. In existing research, deep learning methods with excellent image classification performance are used for classification [23; 57]. However, this method is cumbersome and requires time-consuming sample annotation and complex training processes, making it labor-intensive. After weighing the advantages and disadvantages, we ultimately adopt the vegetation index method to achieve efficient and accurate vegetation extraction, even under shadow conditions. After addressing the issue of shadows, the vegetation index method provides excellent vegetation extraction performance, with vegetation identification levels comparable to those of deep learning methods. This study is based on multidimensional experimental tests to determine a suitable vegetation extraction index for the double-exposure algorithm. By considering shadow intensity differences, the best double-exposure combination under different shadow conditions was identified. The design of this algorithm considers both the strengths and weaknesses of existing related research. The double-exposure data obtained by this algorithm can be adapted to the vegetation index method. Ultimately, the design objective of a simple, direct, targeted, and efficient anti-shadow algorithm based on vegetation extraction was achieved in this study. The double-exposure algorithm is a vegetation extraction method that can mitigate shadow interference. This approach provides a new and effective strategy for precise and rapid FVC calculation, high-precision monitoring of ecological environments, and ground validation of remote sensing products.