# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 2 Method Development
### Study Cases  
The submitted method was tested using UAV-captured images. In this case, the mentioned images were captured with the previously described UAV device; the resolution of each picture is approximately 3800 \(\times\) 2800 pixels. As previously stated, there is no public shadow removal dataset for remote sensing images; thus, the test images were shot at an arbitrary capturing height, as specified in the experimental setup. This work's primary objective for image acquisition was to include concrete and asphalt regions, which are commonly found in urban areas. The main criteria for the study cases selection were that the images depicted urban scenes that contain multiple colors, textures, and a considerable amount of asphalt and concrete regions. The study cases must contain different cast shadow and self-shadow coverage at different proportions. Such parameters are helpful to test the performance of the present method in terms of color correction consistency and texture preservation results. The study cases used in this work are displayed in Figure 5.  
As shown in Figure 5, all study cases contain scenes that accomplish the mentioned criteria, in which each one contains shadows that cover different regions in the urban image. Specifically, Figure 5a-c contain shadows in a range of 40-60%, which are, in this work, defined as high shadow (HS) images. This group of images is tested in order to analyze the color correction results under conditions where shadowed regions cover up a larger area of the scene. In contrast, Figure 5d-f are about 20-25% covered by shadows; in this work, they are defined as slight shadow (SS) images. Moreover, in Figure 5d,e, a manual correction was performed to refine the shadow mask; this is mainly considered for testing the proposed work over images where the bluish effect in shadows is lighter than in others. In the case of those scenes, the shadowed regions are small regarding the HS group, which can present differences regarding the first group of study cases. It is also noticeable that the darker shadows in concrete and asphalt regions tend to increase the displacement to blue wavelengths in such regions.  
Thus, to compare the present method, we selected two deep learning works: The first one is presented by [PERSON] et al. [16], which develops a Shadow Matting Generative Adversarial Network (SMGAN) to synthesize realistic shadow mattings from a given shadow mask and shadow-free image. The second is the method proposed by [PERSON] et al. [35], using  
Figure 5: Test images, urban scenes covered by about 40–60% of shadows: study cases 1 (**a**), 2 (**b**), and 3 (**c**). The second group of urban scenes covered by about 20–25% of shadows: study cases 4 (**d**), 5 (**e**), and 6 (**f**).  
a GAN with a proposed SynShadow, a large-scale synthetic shadow/shadow-free/matte image triplets dataset, and a pipeline to synthesize images. Both methods were implemented as end-to-end shadow detection and removal. In the case of the second group of works, the methodologies were implemented by using the same shadow masks computed and used for our proposal. The methods tested were a shadow removal algorithm proposed by [PERSON] et al. [25], which is based on an illumination correction algorithm, and the work published by [PERSON] and [PERSON]. [33], a local color transfer method that uses the CIE L*a*b color space.