## 3 Test and Results
### Shadow Detection Results  
As described in the previous section, this work conducts a comparative analysis of models. For CADDN and MSADNet, the GitHub (v3.1) code provided by the authors was implemented, while the other models were implemented using Python 3.10, maintaining the same experimental setup to execute the proposed work. It is important to note that the same hardware and GPU were used to run all tests. Input images were resized to (224 \(\times\) 224) for all evaluated methods. All methods were implemented and tested using the test dataset, and the comparative results are presented in Table 1. The metrics considered for the comparison include OA, IoU, F1-Score, and BER. These metrics are highly relevant for demonstrating the quality of the segmentation, including results related to an unbalanced dataset. It can be observed from Table 1 that the proposed SDKU-Net model consistently outperforms the tested methods across all evaluation metrics. For instance, in terms of OA, SDKU-Net achieves the highest score, demonstrating its reliability in correctly classifying both shadow and non-shadow regions. Although the improvement in OA is relatively modest compared to some methods, the IoU metric highlights a significant advantage, as SDKU-Net better captures shadow regions with a higher overlap between predictions and ground truth compared to other methods. In the case of the F1-Score and BER, SDKU-Net shows robustness in minimizing both false positives and false negatives. These results are particularly important for imbalanced datasets, where errors in either class (shadow or non-shadow) can impact overall performance. The lower BER indicates a well-balanced performance, reducing misclassifications across both classes. Following the quantitative analysis, a comparative evaluation of the segmentation results was conducted through visual inspection. By examining the output images generated by each model, the qualitative performance and practical implications of the differences shown in Table 1 were further assessed. This visual comparison provides additional validation of the results, focusing on small regions and intricate shadow areas. This comprehensive evaluation, combining both quantitative and qualitative analyses, reinforces the findings and highlights the robustness of SDKU-Net in effectively detecting shadows across diverse scenarios. For each test, the F1-Score is included to enhance both the visualization and numerical interpretation of individual results. To visually evaluate the performance of our method, we analyzed three representative images from the dataset. These images were selected to illustrate (a) additional detections not present in the ground truth, (b) matches with the ground truth, and (c) a case of unbalanced data. Figure 8 demonstrates the distinct performance of each model in shadow detection, particularly when the ground truth does not account for all shadowed regions, further emphasizing the differences between models. In Figure 8, it can be observed that MSASDNet achieves the highest F1-Score among the compared methods, demonstrating strong performance in the overall segmentation of shadowed regions. However, it exhibits under-segmentation in small regions, particularly at the edges of some shadowed areas. This indicates that while MSASDNet excels in capturing larger structures, it struggles with finer details. DKUNet, on the other hand, shows a noticeable improvement in the segmentation of small shadowed regions, providing a more refined and precise delineation of these areas. This is particularly evident in the smaller shadows near the buildings, where DKUNet accurately captures the intricate details. The proposed method displays some over-segmentation in certain regions, including a significant area in the upper part of the image. However, this over-segmentation corresponds to shrub-covered regions, which are clearly visible as shadows in the color image but were not labeled as shadows in the ground truth segmentation.