# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods  
### U-Net  
The U-Net derives its name from its distinctive \"U\" shape structure, which was conceived and adapted from the FCN network architecture [24]. In contrast to the FCN network, the salient feature of the U-Net is its ability to generate a higher-precision image segmentation model with a reduced quantity of training images, attributable to its unique \"U\" shape structure. The left structure of the network constitutes the encoder, employing convolution and pooling operations to downsample the input image, obtaining contextual information, and generating feature maps to extract rudimentary information. The right structure represents the decoder, executing upsampling operations on the feature maps to acquire more profound features. The central structure is the skip connection component, fusing deep and shallow feature maps procured at the same stage to form a feature-enhanced network. The network framework is implemented using convolution, pooling, activation, and normalization techniques [25]. 1. Convolution: Convolution is the process of filtering the input image or feature map to extract its feature information [26]. In the U-Net, convolution operations usually use a 3 \(\times\) 3 convolution kernel to convolve the input feature map and obtain the output feature map. The convolution operation can be expressed by Equation (1): \[y=f\left(\sum_{i=1}^{n}X_{i}*W_{i}+b\right)\] (1) where \(X_{i}\) represents the input feature map, \(W_{i}\) represents the convolution kernel, \(b\) represents the bias term, \(*\) represents the convolution operation, \(n\) represents the number of convolution kernels, and \(f\) represents the activation function. 2. Pooling: Pooling operations involve the downsampling of input feature maps, decreasing the resolution and dimensions to enhance the computational efficiency and diminish the complexity [27]. Pooling techniques can be categorized into two primary types: max pooling and average pooling. Max pooling slides a fixed-size window across the input feature map, selecting the maximum value within the window for output. This operation accentuates prominent features while effectively reducing spatial resolution. Conversely, average pooling utilizes the average value within the window as output, smoothing the input feature map's information and efficiently lowering the spatial resolution. In the U-Net, 2 \(\times\) 2 max pooling is typically employed, extracting the maximum value from four adjacent pixels in the input feature map. 3. Activation Function: The activation function applies a nonlinear transformation to the outcomes of convolution and pooling processes, bolstering their expressive capabilities. Within the HRU-Net, the rectified linear unit (ReLU) function is employed, offering rapid convergence and enhanced generalization capacity [28]. The ReLU function can be expressed by Equation (2): \[f(x)=max(0,x)\] (2)
4. Normalization Layer: The normalization layer is a prevalent structure in neural networks, responsible for normalizing the input data to stabilize and regulate the distribution. In this study, batch normalization was employed, normalizing each layer's input data within the network [29]. This process results in more stable data distribution, facilitating model training acceleration and enhancing generalization capabilities. 5. Upsampling: Upsampling is the process of enlarging low-resolution feature maps into high-resolution ones [30]. Typically, it is used alongside downsampling as a conventional feature extraction technique. Within the realm of deep learning, two principal techniques are utilized for the operation of upsampling, namely transposed convolution (also referred to as deconvolution) and bilinear interpolation. In the current investigation, our choice fell upon bilinear interpolation to serve as the upsampling module. This preference is rooted in several key factors: Firstly, bilinear interpolation is more computationally efficient and faster compared to transposed convolution.