## 4 Experiment
### Results Discussion  
The shadow detection results for the Airport and Houston datasets are shown in Figure 4. By comparing the shadow detection results with the false-color images, it can be observed that the shadow detection method is effective in identifying shadowed areas. The results of different shadow compensation methods based on the Airport dataset are shown in Figure 5. The MSR globally increases spectral intensity to minimize the  
Figure 4: The shadow detection results. (**a**) Airport dataset. (**b**) Houston dataset. spectral intensity difference between shadowed and non-shadowed areas, but this compensation causes overexposure, distorts spectral features, and blurs the details of ground objects. The SC-CycleGAN demonstrates strong spectral compensation in shadowed areas. However, it is extremely reliant on the distribution of training data, which leads to incorrect reconstruction of spectra for small sample materials and blurred details. The MF relies entirely on exposure to compensate for shadowed areas and cannot solve the problem of nonlinear spectral attenuation. Moreover, due to the gradual reduction of shadow components near non-shadowed areas, this method has an unnatural transition at the shadow boundary. ISR introduces spectral distortion in compensating images, which is caused by the complexity of material composition in high-resolution scenes. In contrast, our method exhibits superior performance in both spectral similarity and visual reality. This allows it to avoid issues such as overexposure, blurred details, and unnatural transitions. Table 1 presents the spectral similarity of different compensation methods on the Airport dataset using MAE, RMSE, and SAM. The spectral compensation effect of materials in shadowed areas can only be measured based on the spectra of the same material in non-shadowed areas. For this purpose, 20, 50, and 100 pixels are randomly selected from the shadowed and non-shadowed areas to calculate these metrics and their average values. Our method achieves the best values across all metrics, demonstrating its superior performance in shadow compensation on real-world hyperspectral imagery. Figure 5: Spectral reflectance comparison before and after shadow compensation on the Airport dataset. (**a**) Original image; (**b–f**) results from MSR [43], SC-CycleGAN [16], MF [8], ISR [44], and our method. The red and blue dots represent the selected pixels in the shadowed and non-shadowed areas, respectively. * The spectral values of MSR are divided by 5 to facilitate alignment with the coordinate axes of other methods. Figure 6 displays the result of different shadow compensation methods on the Houston dataset. The MSR increases the overall spectral intensity, to some extent compensating for material details in shadowed areas, thereby diminishing the difference between shadowed and non-shadowed regions. The SC-CycleGAN alters the spectral characteristics of different materials, resulting in unreasonable visual effects. Although MF preserves the original features of ground object details, the compensation area still exhibits a certain degree of spectral distortion overall. The ISR causes significant spectral distortion, which is primarily attributed to the diversity and complexity of materials in the scene. In contrast, our method effectively compensates shadowed areas without introducing spectral distortion, and the overall false-color image shows consistent and reasonable colors. Unlike MF, our fusion method takes into account spectral variance to better capture spectral features. Figure 7 demonstrates a detailed comparison of shadow compensation in different channels of the Houston dataset, and our method demonstrates superior visual results. In channel 108, for example, the distinction between shadowed and non-shadowed areas is nearly imperceptible, indicating that the proposed method effectively maintains spectral continuity and visual consistency in hyperspectral images. Figure 6: Shadow compensation results of different methods on the Houston dataset.