# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 5 Discussion  
From the results, we observe that a thorough study is necessary to perform when detecting shadows in drone images. Our initial investigation demonstrated that the non-drone ISTD images could not be used to obtain shadow detection in drone images, as shown in Table 4. We expect similar unsatisfactory results as those in Table 4 when using a model created based on other non-drone datasets that were widely used in the literature (i.e., SBU and UCF). This is because the images of the non-drone dataset are insufficient to model the complex shadows in drone images.  
Furthermore, the use of automatic thresholding in U-Net architecture is necessary considering the variant aerial images. Even though the comparison of the results between manual and automatic thresholding in Table 5 is not apparent, the automatic Otsu's thresholding is necessary to adaptively distinguish shadow and no-shadow pixels according to different images. Based on the visual observation in Figure 7, regardless of whether using manual or automatic thresholding, U-Net shadow detection has difficulty identifying the boundary of shadow regions correctly. The unsmooth shadow boundary detection is clearly visible in Figure 7c,d.  
As for the refinement methods, dense CRF outperforms the two morphological operations, as the results show that dense CRF achieves the best performance, quantitatively and qualitatively (refer to Figures 8h and 9h). Despite its performance in refining the final  
Figure 9: Visual comparison of refinement methods on Test Image 2: (**a**) input image, (**b**) ground truth, (**c**) without refinement, (**d**) opening using rectangle kernel shape (**e**), opening using cross kernel shape, (**f**) closing using rectangle kernel shape, (**g**) closing using cross kernel shape, and (**h**) dense CRF. Dense CRF performs considerably well, as most of the white noise is removed.  
shadow masks, it erases some small shadow areas. Furthermore, it smoothens the boundary and fills up the non-shadow area into a shadow area. For the opening operations, although it incompletely eliminates the small noises, lots of small white noise in several areas is removed (refer to Figure 8d,e and Figure 9d,e). Both kernel shapes show the same effect on the predicted mask. As for the closing operation, it is unable to improve the predicted mask, as it is unable to remove most of the white noise (refer to Figure 8f,g and Figure 9f,g). It is consistent with the purpose of the closing operation implementation, which is to close gaps in white regions.  
Overall, the poor performance of shadow detection in this study is contributed by three factors: (i) unclear shadow boundary due to unstructured objects, such as trees, (ii) dark color pixels, such as tarmac, misclassified as shadows (False Positive), and (iii) failed detection (False Negative) of small shadowed regions cast by objects that appear small, such as vehicles, humans, and bushes. These problems are non-trivial when dealing with high-spatial-resolution images captured at high altitudes. Thus, a careful investigation should be conducted when detecting shadows in drone images.