# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
###### Abstract  
Recent advances in deep learning have shown remarkable performance in road segmentation from remotely sensed images. However, these methods based on convolutional neural networks (CNNs) cannot obtain long-range dependency and global contextual information because of the intrinsic inductive biases. Motivated by the success of Transformer in computer vision (CV), excellent models based on Transformer are emerging endlessly. However, patches with a fixed scale limit the further improvement of the model performance. To address this problem, a dual-resolution road segmentation network (DCS-TransUperNet) with a features fusion module (FFM) was proposed for road segmentation. Firstly, the encoder of DCS-TransUperNet was designed based on CSwin Transformer, which uses dual subnetwork encoders of different scales to obtain the coarse and fine-grained feature representations. Secondly, a new FFM was constructed to build enhanced feature representation with global dependencies, using different scale features from the subnetwork encoders. Thirdly, a mixed loss function was designed to avoid the local optimum caused by the imbalance between road and background pixels. Experiments using the Massachusetts dataset and DeepGlobe dataset showed that the proposed DCS-TransUperNet could effectively solve the discontinuity problem and preserve the integrity of the road segmentation results, achieving a higher IoU (65.36% on Massachusetts dataset and 56.74% on DeepGlobe) of road segmentation compared to other state-of-the-art methods. The considerable performance also proves the powerful generation ability of our method. remote sensing image; road segmentation; CSwin Transformer; dual scales; long-range contextual dependencies +
Footnote †: journal: Medical  
divide the road extraction methods into two categories: pixel-based and object-oriented. In method-based pixels, the differences between spectral features are fully utilized to identify roads. For example, [PERSON] et al. [8] introduced a method-based Otsu threshold to distinguish the road, the output is a binary image composed of road and non-road road pixels. [PERSON] et al. [9] proposed a method based on the spectral angle for road extraction, which distinguished the road pixels by the spectral angle threshold. In object-oriented road segmentation, the remote sensing imagery was clustered and split into different areas, regarded as the target of road segmentation. A method based on region-growing was designed by [PERSON] et al. [10] to extract roads from synthetic aperture radar imageries. Furthermore, [PERSON] and [PERSON] [11] integrated Markov random field based on object-oriented methods. The methods mentioned above have attained excellent performance under certain circumstances. However, these manual methods designed for specific scenarios have poor generalization ability, and many thresholds' parameters demand a tune up. Unlike the traditional method, the method based on deep learning depends on the superiority of feature representation learning and the ability for parameter sharing to distinguish roads automatically and efficiently. For instance, [PERSON] et al. [12] introduced a CNN-based network with the multifeatured pyramid to obtain more road information details because this structure validly expands the receptive field of the features. A network-in-network structure combined with a full convolution neural network (FCN) successfully was proposed by [PERSON] et al. [13], which utilized the superiority of large context windows to achieve faster detection. In addition, [PERSON] et al. [14] introduced a hybrid CNN combining multiple subnetworks to detect multi-scale roads. These methods based on CNNs demonstrated superior performance in terms of automatic road segmentation. However, due to the intrinsic inductive biases, the specific convolution layer fails to obtain a global receptive field. So, their performance will face great resistance to further breakthroughs.