# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 2 Related Works  
Computer vision and machine learning strongly contribute to satellite image classification. Focusing on remote-sensing methods for impervious surface extraction, machine-learning approaches can be divided into three groups: (i) pixel-based, (ii) texture-based and (iii) semantic segmentation algorithms [10]. _Pixel-wise classifiers_ typically exploit the spectral signature by relying on ad hoc features. Usually, they leverage a similarity measure to measure the spectral differences between impervious surfaces and other ground objects. Most commonly used indexes are Normalised Difference Built-up Index (NDBI) [11; 12], Normalised Difference Vegetation Index (NDVI) [11; 13], Index-based Built-up Index (IBI) [14], Normalised Impervious Surface Index (NISI) [15], Combination establishment index (CBI) [16] and Corrected Normalised Difference Impervious Surface Index (MNDISI) [17]. These methods are usually computationally not demanding. The flip side is that pixel-based approaches ignore spatial context information. This implies that they can easily be misled by the noise and the within-class variability, causing a salt-and-pepper effect within the classification result. Instead, the _texture-based approaches_ do not rely on the spectral information of imagery but rather exploit  
Figure 1: High-resolution Pleiades satellite image of a dense urban area in Rome, Italy (**a**), and the corresponding imperviousness maps from ISPRA (**b**), ESRI (**c**) and DynamicWorld (**d**). For imperviousness maps, red indicates impervious areas while green corresponds to non-impervious ones. spatial information among neighbouring pixels to overcome the noise better and to capture different types of spatial structures. Given a set of features to take into account, different classification methods have been used to divide pixels into impervious and permeable surfaces. Commonly used classifiers include Support Vector Machines [18; 19], artificial neural networks [20], decision trees [21] and random forest [22]. The wide variety of solutions so far described highlights the difficulty in finding the best combination of features that suit the classification task due to the high variability of impervious surfaces' appearance on remote-sensing imagery [1]. Therefore, this task can benefit from data-driven feature-learning approaches and end-to-end model training provided by _semantic segmentation_ algorithms [23]. In the past decade, deep learning has proved to be effective on this task, with Convolutional Neural Networks (CNN) outperforming many traditional machine-learning solutions. Nearly all state-of-the-art architectures for semantic segmentation follow principles stated in [24], where semantic segmentation using Fully Convolutional Networks (FCN) are demonstrated to be able to achieve impressive results. The main idea consists of modifying traditional CNN so that the output is no longer a probability vector but rather a probability map. That was possible by replacing standard fully connected layers of CNNs with fully convolutional layers to \"densify\" the single-vector output of a traditional CNN. A second feature was the use of transposed convolutions, also called deconvolutions. A deconvolution layer is used for up-sampling a feature map and obtaining a prediction of the same size as the input image [25]. The third feature was the skip connections to combine dense prediction at shallow layers and coarse predictions at deep layers, improving segmentation details. On this line, several architectures have been so far proposed. The U-Net architecture [7], designed for biomedical image segmentation, introduced the encoder-decoder paradigm for up-sampling gradually from lower-size features to the original image size. Since then, almost all CNN models for semantic segmentation have some form of encoder-decoder structure. The encoder reduces the spatial resolution of the input and creates lower-resolution feature mappings that are highly effective at classifying objects.