Road extraction is an important task in remote sensing image information extraction. Recently, deep learning semantic segmentation has become an important method of road extraction. Due to the impact of the loss of multiscale spatial features, the results of road extraction still contain incomplete or fractured results. In this article, we proposed a deep learning model, which is called the dense-global-residual network that reduces the loss of spatial information and enhances context awareness. In the dense-global-residual network, the residual network is used to extract the features at different levels. To obtain more abundant multiscale features, a dense and global spatial pyramid pooling module based on Atrous Spatial Pyramid Pooling is built to perceive and aggregate the contextual information. The proposed method obtains better results on the GF-2 road dataset and public Massachusetts road dataset of aerial imagery. In order to prove the effectiveness of our method, we compared with four methods, such as DeepLabV3+, U-net, D-LinkNet, and coord-dense-global model, and found that the accuracy of our method is considerably better. Moreover, the dense-global-residual network can also effectively extract roads, especially trees and building shadows that occlude the road. In addition, our method can successfully extract roads in regions of different development levels in universality experiments. This indicates that the proposed method can effectively maintain the completeness and continuity of roads and improve the accuracy of road segmentation from high-resolution remote sensing images.

中文翻译：

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使用基于密集连接空间特征增强金字塔的方法从高分辨率遥感图像中自动提取道路

道路提取是遥感图像信息提取中的一项重要任务。近年来，深度学习语义分割已成为道路提取的重要方法。由于多尺度空间特征丢失的影响，道路提取的结果仍然包含不完整或破碎的结果。在本文中，我们提出了一种深度学习模型，称为密集全局残差网络，可以减少空间信息的丢失并增强上下文感知。在dense-global-residual网络中，残差网络用于提取不同层次的特征。为了获得更丰富的多尺度特征，构建了基于 Atrous Spatial Pyramid Pooling 的密集全局空间金字塔池化模块来感知和聚合上下文信息。所提出的方法在GF-2道路数据集和马萨诸塞州公共道路航空影像数据集上获得了更好的结果。为了证明我们方法的有效性，我们与 DeepLabV3+、U-net、D-LinkNet 和 coord-dense-global 模型等四种方法进行了比较，发现我们方法的准确性要好得多。此外，密集全局残差网络还可以有效地提取道路，尤其是遮挡道路的树木和建筑物阴影。此外，我们的方法可以在普适性实验中成功提取不同发展水平地区的道路。这表明所提出的方法可以有效地保持道路的完整性和连续性，提高高分辨率遥感图像道路分割的准确性。