###### Abstract  
Road segmentation from high-resolution (HR) remote sensing images plays a core role in a wide range of applications. Due to the complex background of HR images, most of the current methods struggle to extract a road network correctly and completely. Furthermore, they suffer from either the loss of context information or high redundancy of details information. To alleviate these problems, we employ a dual branch dilated pyramid network (DPBFN), which enables dual-branch feature passing between two parallel paths when it is merged to a typical road extraction structure. A DPBFN consists of three parts: a residual multi-scaled dilated convolutional network branch, a transformer branch, and a fusion module. Constructing pyramid features through parallel multi-scale dilated convolution operations with multi-head attention block can enhance road features while suppressing redundant information. Both branches after fusing can solve shadow or vision occlusions and maintain the continuity of the road network, especially on a complex background. Experiments were carried out on three datasets of HR images to showcase the stable performance of the proposed method, and the results are compared with those of other methods. The OA in the three data sets of Massachusetts, Deep Globe, and GF-2 can reach more than 98.26%, 95.25%, and 95.66%, respectively, which has a significant improvement compared with the traditional CNN network. The results and explanation analysis via Grad-CAMs showcase the effective performance in accurately extracting road segments from a complex scene. remote sensing imagery; transformer mechanism; dilated convolution; road segmentation; explanation analysis +
Footnote †: journal: Medical Imaging  
boundaries and significant obstruction from roadside trees. Therefore, with increasing image resolution, the intra-class differences of roads become more prominent, posing a greater challenge for road interpretation tasks. In recent years, the transformer mechanism has gained increasing popularity in the field of optical remote sensing imagery interpretation, owing to its demonstrated advantages in natural language processing tasks. The transformer mechanism achieves global context information by utilizing multi-head attention, enabling it to establish connections between different patches of the input sequence and capture comprehensive contextual details [2]. However, there are several challenges when directly applied to the aspect of road extraction from HR remote sensing images. The most significant problem arises from the transformer's limited ability to capture fine-grained local features, resulting in a lack of detailed information in the outputs and leading to blurry or less sharp features. Transformers are primarily designed to focus on global context and long-range dependencies, which may not be well-suited for capturing the intricate local details inherent to HR remote sensing images. As a remedy, the integration of local feature pyramids and global context information becomes indispensable to boost the performance of road segmentation from HR remote sensing images. In this part, we introduce a dual parallel branch fusion network (DPBFN) to address the challenges of broken and incomplete road segmentation results. The DPBFN tackles issues related to shadows and occlusions by employing a dual branch network architecture. The network comprises a transformer branch and a residual multi-scaled dilated convolution network branch, fused together through a fusion module. The contributions can be summarized as follows:  
1. We employed a dual parallel branch architecture to synthesize the features capturing from depth convolutional and transformer branches, which is able to maintain more unique characteristics of the road network. Moreover, dual branches features as a complementary can discriminate the road segments by the spatial and spectral dependency in the feature space for better inferring the occluded roads. 2.