基于Swin Transformer的遥感影像道路提取方法

在先进的交通系统中，道路提取是最重要的任务之一。高分辨率遥感影像道路区域的提取具有复杂的背景和道路网络的异质性、高类间差异和低类内差异等特点。近几年来，卷积神经网络(CNN)在道路提取方面取得了里程碑式的进展。虽然CNN已经取得了很好的发展，但是由于卷积运算的局域性，网络无法很好地学习全局和长程语义信息交互。本文提出了Swin Transformer Unet，它结合了带有跳跃连接的U型编解码器结构和带有移位窗口的Swin Transformer模块。为了获得更好的性能，本文采用了数据增广、数据预处理等技术。本文选取马萨诸塞州道路数据集作为数据集进行道路提取实验，结果表明，所提出的网络在遥感图像道路提取中的性能优于其他U形网络，可以实现遥感影像道路的精确提取。

Improving Road Extraction for Autonomous Driving Using Swin Transformer Unet

One of the most important tasks in the advanced transportation systems is road extraction. Extracting road region from high-resolution remote sensing imagery is challenging due to complicated background and rural road networks that have heterogeneous forms with low interclass and high intraclass differences. In the past few years, convolutional neural networks (CNNs) have achieved milestones in road extraction. Although CNN has achieved excellent performance, global and long-range semantic information interaction cannot be learned well due to the locality of convolution operation. In this paper, we propose Swin Transformer Unet which combined U-shaped architecture with hierarchical Swin Transformer with shifted windows. Moreover, we improve the loss function to make it more suitable for our road extraction task. The techniques of data augmentation and data preprocessing are used in order to get better performance. The Massachusetts roads dataset is chosen as the dataset to carry out the experiment of road extraction, and the result shows that this model outperforms other U-shaped networks of road extraction from re-mote sensing images.