## 4 Experiment
### Results Discussion
(**a**) Original image; (**b**–**f**) results from MSR [43], SC-CycleGAN [16], MF [8], ISR [44], and our method. To further explore the spectral fidelity of shadow compensation methods for different materials, we analyze the spectral similarity between the shadow materials processed by different compensation methods and the non-shadow materials in the original image. Figure 8 shows the spectral similarity metrics between the compensated shadow pixels and the non-shadowed pixels in the original image of different materials in the Houston dataset. The computation of the metrics is based on Figures 3b and 4b, where the average spectral value of all pixels in the same material and state represents the spectrum of the material in that state. Except for the slightly higher SAM of our method in Figure 8j compared to the MF method, we achieve the best spectral similarity metrics among all other materials. Compared with other methods in terms of spectral similarity metrics in different materials, our method has a more stable spectral fidelity ability. Figure 7: The shadow compensation results for the Houston dataset are demonstrated through pseudocolor images across various channels (36, 72, 108, and 144). This includes the Original image, MSR [43], SC-CycleGAN [16], MF [8], ISR [44], and our method. Shadow compensation, as an upstream task, aims to improve the quality of hyperspectral images and facilitate the development of higher-level visual analysis of downstream tasks. To demonstrate the spectral fidelity of our method in both non-shadowed and  
Figure 8: Compute the spectral fidelity of different materials processed by different compensation methods on the Houston dataset. (**a**) Healthy grass. (**b**) Stressed grass. (**c**) Trees. (**d**) Water. (**e**) Residential. (**f**) Commercial. (**g**) Road. (**h**) Highway. (**i**) Railway. (**j**) Parking lot 2. The values of SAM are divided by 4 for display purposes. Lower MAE, RMSE, and SAM indicate better performance. shadowed regions, we validate it using an improved downstream classification task. We randomly select 15% of samples from each category located in the non-shadowed area of the original image to form a training set. The SVM method is utilized to test samples in shadowed and non-shadowed areas separately. Table 2 shows the classification performance of the SVM on the original image and the images processed by MSR, SC-CycleGAN, MF, ISR, and our method, separately, including the classification performance of non-shadowed areas and shadowed areas. The MSR causes severe spectral distortion in both non-shadowed and shadowed areas. SC-CycleGAN, MF, and our method avoid spectral distortion in non-shadowed areas. In shadowed areas, the classification results of the original image are not ideal. Compared with other methods, the shadow categories processed by our method perform the best in terms of classification performance. Other methods performed poorly, especially in the challenging 'Water' category, while our method improves the classification performance. Similarly, in the categories of 'Residential', 'Road', and 'Railway', our method significantly improves the classification accuracy in comparison to other methods. Compared with the original image and the suboptimal method, our method improves the OA of the shadowed area by 210.14% and 14%, respectively. Overall, our method can effectively compensate for shadowed areas while ensuring that non-shadowed areas are not distorted. This is precisely because the spectral characteristics of the shadowed materials processed by our method are more similar to those of well-lit materials, which contributes to a better classification performance and proves the effectiveness of our method.