## 4 Experiment
### Results Discussion
To deeply analyze the reasons for the superiority of our shadow compensation method in solving the nonlinear spectral attenuation problem, we conducted ablation experiments on the fusion weights in the multi-exposure fusion method, as shown in Figure 9. Two pixels located at (1729, 76) and (1738, 68) are selected to represent the same material in shadowed and non-shadowed states, respectively. A portion of the image from column 1557 to column 1905 in the Houston dataset is cropped for display, with the two pixel positions highlighted with a blue dot and a red dot, respectively. As shown in Figure 9a,c,f, without considering spectral variance, the spectral curves of the compensated shadowed areas are only linearly enhanced compared to the original spectra. This enhancement ignores the spectral characteristics of materials at some peaks. In fact, for materials affected by shadows, nonlinear attenuation is more severe in spectral bands with high values and large fluctuations. The experimental results using spectral variance indicate that the spectral curve of the material in the compensated shadowed area more accurately reflects the spectral characteristics of the materials, especially at several peaks with severe spectral attenuation. In summary, our compensation method utilizes the correlation between bands to better address the nonlinear attenuation caused by shadows and restore the spectral characteristics of the material. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Metrics** & **Original** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours** \\ \hline MAE (20 px) & 0.0344 & 0.1299 & 0.0114 & 0.0150 & 0.0113 & **0.0082** \\ RMSE (20 px) & 0.0382 & 0.1383 & 0.0119 & 0.0159 & 0.0131 & **0.0099** \\ SAM (20 px) & 0.6867 & 0.0856 & 0.0598 & 0.1789 & 0.1655 & **0.0490** \\ \hline MAE (50 px) & 0.0333 & 0.1362 & 0.0089 & 0.0137 & 0.0103 & **0.0077** \\ RMSE (50 px) & 0.0369 & 0.1462 & **0.0094** & 0.0146 & 0.0122 & **0.0094** \\ SAM (50 px) & 0.6702 & 0.0984 & 0.0513 & 0.1725 & 0.1615 & **0.0500** \\ \hline MAE (100 px) & 0.0346 & 0.1394 & 0.0101 & 0.0140 & 0.0102 & **0.0078** \\ RMSE (100 px) & 0.0383 & 0.1497 & 0.0106 & 0.0149 & 0.0121 & **0.0095** \\ SAM (100 px) & 0.6760 & 0.0988 & 0.0550 & 0.1749 & 0.1628 & **0.0511** \\ \hline \end{tabular}
\end{table} 
Table 1: Spectral similarity of different compensation methods on the Airport dataset. \begin{table}
\begin{tabular}{c c c c c c c|c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Accuracies of Non-Shadowed (\%)**} & \multicolumn{6}{c}{**Accuracies of Shadowed (\%)**} \\ \hline
**Class Name** & **Origi.** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours** & **Origi.** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours** \\ \hline Healthy grass & **98.08** & 0.00 & 97.75 & 97.81 & 97.91 & 97.86 & 12.92 & 0.00 & 25.28 & **54.49** & 16.85 & 51.12 \\ Stressed grass & 98.27 & 0.00 & 98.32 & **98.43** & 98.38 & 98.22 & 18.29 & 0.00 & 32.93 & 54.88 & 47.56 & **75.00** \\ Synthetic grass & 88.46 & 0.00 & 88.49 & **88.79** & 88.46 & 88.03 & - & - & - & - & - & - \\ Trees & 98.12 & 0.00 & 97.96 & 98.37 & **98.38** & 98.17 & 41.13 & 0.00 & 42.74 & **75.00** & 37.10 & 67.74 \\ Soil & 91.42 & 0.00 & 91.46 & 86.15 & 85.25 & **91.82** & - & - & - & - & - & - \\ Water & 98.11 & 0.00 & 98.11 & 97.67 & 97.67 & **98.53** & 12.50 & 0.00 & 5.00 & 2.50 & 0.00 & 22.50 \\ Residential & 72.73 & 0.00 & 72.76 & **75.27** & 74.73 & 71.79 & 10.00 & 0.00 & 16.25 & 51.25 & 18.75 & **71.25** \\ Commercial & 55.34 & 11.19 & 54.51 & 55.84 & **56.14** & 55.72 & 53.73 & 0.00 & 69.96 & 71.49 & 70.61 & **74.78** \\ Road & 67.48 & 0.00 & 67.39 & 66.86 & 66.69 & **68.19** & 8.11 & 18.92 & 2.70 & 29.73 & 16.22 & **56.76** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of different methods in both shadowed and non-shadowed states after training on non-shadowed samples on the Houston dataset. Figure 9: Fusion weight ablation study based on the Houston dataset. (**a**) Only contrast. (**b**) Only variance. (**c**) Only exposure. (**d**) Contrast and variance.