# Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET
## 2 Related Studies
### Shadow Detection Methods in Drone Images
#### 2.2.3 Machine Learning  
Shadow detection using machine learning, a technique where a model is built and fit into data to accomplish a task, is a method that has been preferred recently. This is mainly because machine learning techniques require less knowledge of shadow properties and offer more flexibility in detection compared to older techniques. Machine learning techniques can be divided into three more categories, which are unsupervised learning, supervised learning, and deep learning. Unsupervised learning is popular due to its simplicity of implementation, and it does not require sample data to train the model, which saves a lot of time on data preparation. Shadow detection using unsupervised learning is often implemented by clustering technique, where the pixels of the images are grouped by their similarities, and a cluster that possibly represents shadow is selected. An algorithm that can be seen to be popular for performing shadow detection in aerial images is K-Means. Ref. [20] used improved K-Means, which is K-Means-AP, to solve the drawbacks of traditional K-Means, where the random number of clusters produces a random result. Another implementation was by [21], which compared the clustering results of two algorithms, K-means and Gaussian Mixture Model (GMM), after preprocessing the image using morphological operations. While using unsupervised learning guaranteed some benefits, the accuracy of the detection still needed improvement, as it was prone to noise [21]. Compared to unsupervised learning, supervised learning requires training samples to build classifiers, which usually produce more accurate detection in classification. The famous model used in shadow detection based on supervised learning is the Support Vector Machine (SVM). SVM is used to generate an initial map containing the classification of shadow class and non-shadow class. Various enhancements were also included in SVM, such as the implementation of LSSVM to reduce cross validation error [22] and adding Extended Random Walker to reduce the noisiness result of SVM [23]. As the machine learning field has been widely discussed to solve problems in visual information processing, implementation of deep learning in shadow detection has been a growing discussion, considering there are still many weaknesses shown in traditional unsupervised and supervised learning. Much research has discussed the implementation of deep learning in shadow detection, specifically in trying different neural network architectures and adding enhanced modules to achieve state-of-the-art results. When using drone images, deep learning has always been a consideration, as they usually produce very high-resolution images with complicated shadow areas. In deep learning, shadow detection is considered a semantic segmentation task, where each pixel is assigned as either shadow or non-shadow. Few works related to shadow detection in aerial images implementing deep learning have been studied. One of them proposed DSSDNet, a network that chose a deeply supervised CNN as its network architecture with the implementation of DSPF to improve model performance [10]. Alongside proposing a new model with enhancement, this work also aimed to solve shadow detection in aerial image problems related to insufficient training datasets by creating a publicly available dataset called AISD. The images were captured using drones flying at high altitudes; thus, many small shadowed regions cast by objects such as vehicles are not clearly noticeable and are not labeled. This contribution allows many researchers to propose and test state-of-the-art methods using this dataset, which widens the possibilities for different aspects of enhancement within this topic. One of the research projects which used the AISD dataset was by [4], who proposed the Edge-aware Spatial Pyramid Fusion network (ESPFNet) for enhancing the detection of salient shadows. One of the main components in this network is the parallel spatial pyramid structure, which extracts multiscale features from the input image.