# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 1 Introduction  
As technology has grown in this modern age, small, unmanned aircraft, more widely known as drones, have achieved tremendous growth from being just a mere toy to contributing to solving many problems in various industries. The study of emerging drone technologies and image analysis has been an interesting topic, as it offers opportunities for improvement in numerous disciplines. Detecting shadows is one of the problems that is widely discussed in the field, as shadows are frequently visible in images captured by drones, and hence become obstacles. Currently, a lot of studies on object detection and tracking in drone images listed the presence of shadows as the reason for errors in their findings [1]. Shadows consistently cause detection inaccuracies in many image vision applications due to developed algorithms failing to tackle shadow properties in images. Thus, this problem remains unsolved, and more efforts are necessary to improve shadow detection, considering high-resolution drone images pose complex shadows. As many researchers treated shadows as obstacles, others benefited from the shadows. The presence of shadow can be utilized in some applications, such as object height estimation through drone images [2]. Unfortunately, this possibility is limited due to the limitations of existing shadow detection methods. The early implementations of shadow detection in drone images mainly rely on shadow image properties and the physical properties of surroundings. These methods require more effort from researchers to study the suitability of different properties and are prone to errors resulting from technical malfunctions. Solutions using machine learning have been introduced to many computer vision problems, including shadow detection as an alternative to conventional methods. The early solution was to use unsupervised learning, and it was enhanced to supervised learning to increase accuracy. However, therewere still disadvantages in implementing supervised learning, such as the fact that some classifiers might not be flexible enough to categorize a large number of objects from various scenes, which leads to the implementation of deep learning [3]. Several deep learning implementations have been proposed throughout the years, yet many have stated the importance of implementing lightweight networks to increase efficiency. For the study of shadow detection in drone images, another problem arises, which is the lack of drone image datasets with the shadow ground truth. This limits the research on the machine learning methods, as dataset preparation is laborious and time-consuming. Drone images, which are high resolution and tend to contain shadows of different brightness and sizes, bring up more challenges in developing shadow detection algorithms. One of the issues highlighted in the deep learning approaches was the difficulty of relying on a trial-and-error approach to find the optimum threshold value [4]. An unreliable threshold value always results in noises in predicted shadow masks, consequently leading to errors when removing the shadowed regions. In the present paper, we implement a deep learning method to solve shadow detection in a dataset of drone images. We use the established algorithm in machine learning that requires minimal knowledge of shadow or physical properties. Furthermore, given the complicated shadows which are common in drone images, post-processing strategies for handling the inefficiency of finding optimal threshold values and refining the shadow mask are also proposed. In addition, drone images from various sources are collected and labeled due to the lack of shadow annotated datasets to be used in this study. The aim is to widen the study of deep learning methods specifically for shadow detection in aerial images. The contributions of our work are summarized as follows:  
* Implementation of U-Net architecture with a small number of parameters for shadow detection using a small-sized aerial images dataset.