# Remote Sensing Road Extraction by Road Segmentation Network
## 1 Introduction  
Remote sensing road extraction aims to identify the road pixels in the images and complete the binary segmentation of the road. Nowadays, road extraction is needed in many applications [1, 2] such as traffic navigation and urban planning. However, it is difficult to extract the road from the remote sensing images due to increased noise information [3]. The noise information mainly comes from occlusion or shadows of other ground objects, and similar categories. Moreover, the roads are not always regular, and the width and curvature of the roads varies greatly in different scenes. These factors reduce the accuracy of road extraction methods and prevent the restoration of complete road connectivity.  
Recently, numerous methods have been proposed to obtain a road map. According to whether the Convolutional Neural Network (CNN) architecture is used or not, road extraction methods can be divided into two categories: hand-crafted feature methods and CNN methods.  
The hand-crafted feature methods divide the pixels into road and non-road by extracting shallow features and using empirical hypothesis. Early road extraction methods used hand-crafted features (geometry, texture, spectrum, etc.), multiple algorithms (edge detection, tracking, area clustering, etc.), and were combined with empirical assumptions to extract the road in the image. [PERSON] et al. [4] used the width, length, curvature, and pixel intensity of the road as empirical hypothesis information, and the geometric probability model with the maximum posterior probability estimation was established for road extraction. [PERSON] et al. [5] first detected the local uniform area around the pixel to generate the road tree structure, and then a Bayesian decision model was used to obtain thefinal road. [PERSON] et al. [6] used support vector machines as a classifier and considered weight combination of spectral information and shape information to identify road pixels. However, the hand-crafted feature methods depend on the quality of feature selection. These hand-crafted feature methods are rough and depend heavily on prior knowledge.  
With the rapid development of deep learning [7], CNN methods have become the mainstream methods due to the representation power [8]. [PERSON] et al. [9] used CNN to classify pixels, and then a linear integral convolution algorithm was used to enhance the road connection structure. [PERSON] et al. [10] used the variant of ResNet [11] for road segmentation and then designed a road inference algorithm to correct the segmentation results. [PERSON] et al. [12] utilized local and global attention mechanisms to enhance road information in DenseNet. However, CNN methods do not fully excavate the information of context features extracted from the CNN [13], and they do not consider enough about the strip shape features and long distance dependence of roads.  
The road pixels in remote sensing images may be interfered with by the abundant ground such as buildings, trees, and shadows, which may introduce poor road connectivity. It is important to enhance the context information related to the road and suppress the interference information of non-road. In this paper, a road segmentation method is proposed for road extraction with high-resolution remote sensing images. The road information is extracted by enhancing the context characteristics in the segmentation step and maintaining the stripe characteristics in a refinement step. The contributions are as follows:  
1. To enhance road context features, an end-to-end road segmentation network is designed, since road connectivity is easily disturbed by noise.
2. To strengthen the context information belonging to the road, an inter-layer self-attention mechanism is introduced to generate weight maps.
3. To hold the strip information of the road, a refine network with the striping pooling is introduced to refine the results of the segmentation network.