## 4 Discussion  
### Experimental Results Analysis  
The shadow detection results of Images 3-6 demonstrate that [PERSON]'s method has a large area of misdetection in general, which is especially obvious in Images 4-6. Moreover, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Method** & \(P_{s}\)**(\%)** & \(P_{n}\)**(\%)** & \(U_{s}\)**(\%)** & \(U_{n}\)**(\%)** & \(E_{c}\)**(\%)** & \(E_{o}\)**(\%)** & \(\tau\)**(\%)** & \(kappa\) \\ \hline \multirow{9}{*}{9} & [PERSON] & 99.96 & 60.98 & 49.20 & 99.97 & 39.02 & 0.04 & 71.67 & 0.46 \\  & [PERSON] & 99.78 & 66.10 & 52.67 & 99.87 & 33.90 & 0.22 & 75.34 & 0.52 \\  & [PERSON] & 99.62 & 79.69 & 64.98 & 99.82 & 20.31 & 0.38 & 85.16 & 0.68 \\  & [PERSON] & 93.12 & 95.83 & 89.41 & 97.36 & 4.17 & 6.88 & 95.09 & 0.88 \\  & ECA & 88.08 & 98.16 & 94.77 & 95.61 & 1.84 & 11.92 & 95.39 & 0.88 \\  & Ours (MC3) & 98.00 & 89.13 & 77.32 & 99.16 & 10.87 & 2.00 & 91.56 & 0.80 \\  & Ours (CTS) & 95.19 & 96.79 & 91.80 & 98.16 & 3.21 & 4.81 & **96.35** & **0.91** \\ \hline \multirow{9}{*}{10} & [PERSON] & 91.86 & 78.45 & 57.41 & 96.82 & 21.55 & 8.14 & 81.67 & 0.58 \\  & [PERSON] & 98.40 & 70.29 & 51.16 & 99.29 & 29.71 & 1.60 & 77.04 & 0.52 \\  & [PERSON] & 97.59 & 74.53 & 54.78 & 98.99 & 25.47 & 2.41 & 80.07 & 0.57 \\  & [PERSON] & 92.80 & 96.25 & 88.67 & 97.69 & 3.75 & 7.20 & 95.42 & 0.88 \\  & ECA & 88.83 & 98.56 & 95.11 & 96.54 & 1.44 & 11.17 & 96.22 & 0.89 \\  & Ours (MC3) & 97.14 & 79.29 & 59.74 & 98.87 & 20.71 & 2.86 & 83.58 & 0.63 \\  & Ours (CTS) & 92.08 & 98.11 & 93.89 & 97.51 & 1.89 & 7.92 & **96.66** & **0.91** \\ \hline \multirow{9}{*}{11} & [PERSON] & 76.52 & 75.85 & 24.18 & 96.98 & 24.15 & 23.48 & 75.91 & 0.27 \\  & [PERSON] & 94.46 & 71.62 & 25.09 & 99.23 & 28.38 & 5.54 & 73.71 & 0.29 \\  & [PERSON] & 90.68 & 74.53 & 26.38 & 98.76 & 25.47 & 9.32 & 76.01 & 0.31 \\  & [PERSON] & 65.77 & 99.16 & 88.71 & 96.64 & 0.84 & 34.23 & 96.11 & 0.73 \\  & ECA & 63.72 & 99.64 & 94.66 & 96.46 & 0.36 & 36.28 & 96.35 & 0.74 \\  & Ours (MC3) & 93.31 & 77.67 & 29.60 & 99.14 & 22.33 & 6.69 & 79.10 & 0.36 \\  & Ours (CTS) & 86.87 & 99.16 & 91.19 & 98.68 & 0.84 & 13.13 & **98.03** & **0.88** \\ \hline \multirow{9}{*}{12} & [PERSON] & 73.44 & 76.83 & 23.83 & 96.70 & 23.17 & 26.56 & 76.52 & 0.26 \\  & [PERSON] & 49.09 & 73.51 & 15.46 & 93.60 & 26.49 & 50.91 & 71.32 & 0.11 \\ \cline{1-1}  & [PERSON] & 93.42 & 74.96 & 26.91 & 99.14 & 25.04 & 6.58 & 76.62 & 0.32 \\ \cline{1-1}  & [PERSON] & 72.62 & 98.60 & 83.70 & 97.33 & 1.40 & 27.38 & 96.27 & 0.76 \\ \cline{1-1}  & ECA & 42.68 & 99.10 & 82.32 & 94.60 & 0.90 & 57.32 & 94.03 & 0.53 \\ \cline{1-1}  & Ours (MC3) & **96.03** & 68.84 & 23.32 & **99.43** & 31.16 & **3.97** & 71.28 & 0.27 \\ \cline{1-1}  & Ours (CTS) & 92.87 & 97.46 & 78.30 & 99.28 & 2.54 & 7.13 & **97.05** & **0.83** \\ \hline \hline \end{tabular}
**Note:** The values in **bold** indicate the best performance.  
\end{table} 
Table 4: _Cont._it produces some discrete false alarms with fragmented shadow edges, which is caused by its direct single-pixel calculation of the shadow index and application of threshold segmentation. [PERSON]'s method is able to ensure the continuity of shadows by virtue of superpixel segmentation. However, it has some omissions, and some shadows have inwardly concave edges. [PERSON]'s method exhibits a relatively high rate of misdetection, and due to its sole reliance on the NIR band, it fails to differentiate between water bodies and shadows in Image 4. The [PERSON] and ECA methods have some shadow omissions in Images 3-6, and the shadow boundaries are blurred. Our MC3 method has very few shadow misdetections and omissions on the four images and can ensure the complete detection of large and small shadows with smooth and clear shadow boundaries. The quantitative evaluation results also demonstrate the superiority of MC3 with an average overall accuracy of 94.03%, which is an improvement of 21.82%, 6.77%, 2.29%, 2.54%, and 2.74% compared to the [PERSON], [PERSON], [PERSON], [PERSON], and ECA methods, respectively.