## 4 Experiment
### Discussion of Limitations and Future Analysis
\begin{table}
\begin{tabular}{c c c c c c c|c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Accuracies of Non-Shadowed (\%)**} & \multicolumn{4}{c}{**Accuracies of Shadowed (\%)**} \\ \hline
**Class Name** & **Origi.** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours** & **Origi.** & **MSR** & **SC-Cyc.** & **MF** & **ISR** & **Ours** \\ \hline Highway & 43.68 & 0.00 & 43.34 & **45.11** & 43.93 & 43.55 & 4.29 & 0.00 & 3.07 & 78.53 & 6.13 & **85.28** \\ Railway & 60.61 & 0.00 & **61.01** & 53.22 & 51.34 & 60.34 & 3.91 & 0.00 & 3.26 & 47.88 & 8.14 & **67.10** \\ Parking lot 1 & 48.16 & 0.00 & 48.17 & **49.13** & 47.80 & 47.88 & - & - & - & - & - & - \\ Parking lot 2 & 15.46 & 0.00 & 13.20 & 16.35 & **17.23** & 16.35 & 0.00 & 5.00 & 0.00 & 10.00 & 5.00 & **15.00** \\ Tennis court & 85.64 & 0.00 & 85.19 & **85.89** & 85.61 & 85.08 & - & - & - & - & - & - \\ Running track & 99.10 & 0.00 & 99.02 & 89.97 & 98.31 & **99.11** & - & - & - & - & - & - \\ \hline
**OA** & **77.12** & 5.93 & 77.05 & 76.43 & 75.96 & 77.09 & 22.58 & 0.46 & 29.27 & 61.43 & 31.35 & **70.03** \\
**AA** & 71.30 & 6.25 & 71.18 & 70.65 & 70.30 & **71.32** & 16.49 & 2.39 & 20.12 & 47.58 & 22.64 & **58.65** \\
**Kappa** & **75.22** & 0.00 & 75.14 & 74.47 & 73.96 & 75.18 & 7.38 & 0.00 & 15.39 & 53.86 & 17.88 & **64.15** \\ \hline \hline \end{tabular}  
* Higher OA, AA, and Kappa values indicate a better performance, with optimal values in bold. \(\cdot^{\prime}\) represents that there is no such category in the shadowed area.  
\end{table} 
Table 2: Cont. By comparing the running time of our method with those of other methods, and combining the characteristics of our method, we improve the computational efficiency. Because the shadow area is identified when compensating for shadows in our method, we can improve the computational efficiency by only compensating for necessary shadow areas. We optimize the input of the compensation method based on the shadow detection and optimize the Airport dataset from 400 \(\times\) 400 to 180 \(\times\) 185 with (125, 65) as the top left corner and (305, 250) as the bottom right corner. We also optimize the Houston dataset from 349 \(\times\) 1905 to 349 \(\times\) 800 between columns 985 and 1785. Table 3 shows the runtime of different methods based on two datasets. Due to SC-CycleGAN being a training-based deep learning method, its runtime considers the training duration. Our improved method has improved computational efficiency by 57.56% and 56.8% for the Airport and Houston datasets, respectively. Compared to the MF method with the highest efficiency before improvement, it has increased by 38.2% and 41.34%, respectively. With rough optimization of the input data volume, the computational efficiency has been greatly improved. A future direction for performance improvement is to fully utilize detection results, avoid unnecessary resource waste, and improve practical application capabilities. Figure 10: The impact of contextual sensitivity of background on shadow detection. Based on the Airport dataset, the shadow detection result (**a**) of the original; (**b**) with the minority of the background removed; (**c**) with the majority of the background removed. Based on the Houston dataset, the shadow detection result (**d**) of the original; (**e**) with the minority of background removed; (**f**) with the majority of background removed.