# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 1 Introduction  
A shadow is a phenomenon that is presented when a light source is totally or partially obstructed by an object [1]; according to the shadow location, they can be classified into cast shadow (the part that is cast on the ground or other objects by high objects) and self-shadow (the part of the object that is not illuminated) [2]. With the increasing development of remote sensing technology, the shadow effect becomes more noticeable in aerial imaging, which, together with the higher spatial resolution, brings new challenges to the image preprocessing step. Specifically, unmanned aerial vehicles (UAVs) have been increasingly used over the last few years; recently, this technology has taken place in the areas of object detection [3, 4, 5], agriculture [6, 7, 8], and urban zones analysis [9, 10, 11]. Within the applications focused on urban areas, the presence of cast shadows and self-shadows in aerial images might also cause shape distortion in objects and loss of color information [12] since urban surface features are rather complex with a great variety of shadows resulting from occlusion caused by buildings, bridges, and trees [13]. It is also known that in color aerial images, color characteristics are valid descriptors that simplify identifying characteristics of visual interpretation applications [14]; this fact enhances the need for new methodologies for shadow removal that can retrieve color and texture information from aerial images. Shadow removal methodologies pursue obtaining shadow-free images since it can facilitate improving the performance of tasks, such as object recognition, object tracking, and information enhancement [15]; due to the previously stated, several shadowremoval methods have been developed. Such methods are conducted by using close-shot images [16; 17; 18], video surveillance [19; 20; 21], and aerial images [22; 23; 24]. Numerous methods attending to shadow removal in outdoor scenes have been developed. One of the leading strategies applied when performing shadow removal in outdoor scenes is illumination correction [25; 26; 27]. Highlighted examples in published works are methods that execute shadow removal in UAV aerial images using retinex theory [28] as the basis of computing the illumination correction, wherein the work published by [PERSON] et al. [29] computes an improved luminance image and executes an illumination correction based upon the color transfer formula. Other recently published methodology bases itself on the use of the image matting process. The research published by [PERSON] et al. [30] proposes a relighting method that does not rely on user interaction and maximizes the natural appearance of the output relighted images. Although the results are acceptable, the number of erosion-dilation iterations is empirically justified for the tested dataset, mainly consisting of close camera shots. Illumination correction methods usually depict a high consistency in color restoration and shadow boundaries. However, these methods usually maintain the chromatic features of shadowed regions, leading to color differences, especially in urban aerial scenes. As an alternative method when performing the shadow removal, color transfer [31] is presented. The local color transfer methods use spatial similarity to set up a relationship between pixel features of the single image shadows and non-shadows to restore the shadowed regions, in which such relationship predominantly consists of a statistical correlation [32; 33] that can be performed using different color spaces or a single-color feature. Recently in the deep learning field, convolutional neural network (CNN) architectures have been used to enhance the results of these methods [34]. Furthermore, a deep learning-related approach that uses generative adversarial networks (GANs) is used by [PERSON] et al.