## 3 Test and Results
### Shadow Detection Results
This highlights a limitation of the ground truth data rather than an issue with the proposed method itself. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **OA (\%)** & **IoU** & **F1** & **BER (\%)** \\ \hline U-Net & 94.86 & 0.7960 & 0.8864 & 7.17 \\ U-Net++ & 95.36 & 0.8071 & 0.8932 & 7.86 \\ DSSNet & 94.43 & 0.7694 & 0.8697 & 9.76 \\ MSADNet & 95.19 & 0.7835 & 0.8773 & 9.56 \\ CADDN & 95.62 & 0.8195 & 0.9008 & 6.87 \\
**SDKU-Net (Ours)** & 96.48 & 0.8548 & 0.9217 & 5.02 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative performance of shadow detection methods on the AISD dataset. Metrics include Overall Accuracy (OA), Intersection over Union (IoU), F1-Score, and Balanced Error Rate (BER). Regarding building shadows, the proposed method provides a well-defined segmentation, capturing their shapes accurately and avoiding the over-segmentation seen in U-Net and U-Net++. Compared to these models, the proposed method balances segmentation precision and over-segmentation effectively, offering a more robust performance in both small and large shadowed regions. Finally, it is worth noting that while the F1-Score is slightly lower for the proposed method compared to MSASDNet, the qualitative results suggest that it addresses certain challenges such as over-segmentation and the inclusion of fine details more effectively than other methods. The second sample focuses on the comparison of results with the ground truth. Figure 9 presents the segmentation outputs from each tested method, including the proposed approach. Figure 8: Qualitative comparison of shadow detection results for different methods. (**a**) Original image, (**b**) ground truth, (**c**) U-Net (F1 = 0.9095), (**d**) U-Net++ (F1 = 0.9095), (**e**) DSSNet (F1 = 0.9120), (**f**) MSASDNet (F1 = 0.9352), (**g**) CADDN (F1 = 0.9045), (**h**) proposed method (F1 = 0.9293). U-Net and U-Net++ demonstrate consistent performance, though both methods tend to over-segment small regions and fail to capture intricate shadow details, particularly in the areas surrounding the building. DSSNet reduces the extent of over-segmentation but sacrifices the segmentation of finer shadow details, which are critical for precise shadow delineation. MSASDNet effectively avoids over-segmentation and maintains consistency in segmenting larger shadow regions. However, similar to the previous sample, it misclassifies intricate shadow details, leading to incomplete segmentation in complex shadowed areas. CADDN successfully captures intricate shadow details but fails to retain inner details of building shadows, resulting in incomplete segmentation within larger structures. The proposed method, DKU-Net, achieves accurate shadow segmentation by effectively capturing intricate shadowed areas and providing improved segmentation of building shadows. However, some over-segmented regions remain present. While several of these over-segmented areas correspond to small shadowed regions visible in the original color image but omitted in the ground truth, others do not represent actual shadows and are  
Figure 9: Comparison of shadow segmentation results for the second sample: (**a**) original image, (**b**) ground truth, (**c**) U-Net (F1: 0.9250), (**d**) U-Net++ (F1: 0.9374), (**e**) DSSNet (F1: 0.8912), (**f**) MSASDNet (F1: 0.9443), (**g**) CADDN (F1: 0.9272), (**h**) proposed method (DKU-Net, F1: 0.9509). an artifact of the segmentation process. This highlights the challenge of balancing precision and over-segmentation in complex shadowed scenes. Overall, DKU-Net demonstrates a balanced performance by addressing intricate shadow details while maintaining accurate segmentation of building structures, albeit with room for improvement in minimizing over-segmentation. In the third sample, an unbalanced data image is considered, as shown in Figure 10. It is evident that the shadow mask represents 5.55% of the total image pixels, which indicates a noticeable level of data imbalance.