Geometry and texture noise make it difficult to accurately describe road image rules, which leads to the low degree of automation of traditional template matching algorithms based on internal texture homogenization. We propose a semi-automatic road extraction method based on multiple descriptors to improve the degree of automation while ensuring the accuracy of road extraction. This method aims to address the problems of incomplete road image geometric information and poor homogeneity of internal road texture. The multiscale line segment orientation histogram model and sector descriptor are established. Road points are tracked by interpolation and extension, and postprocessing is used to fit the tracking points and extract the road routes. In this article, high-resolution remote sensing images of different types, different resolutions, and different scenes are selected, and the roads exhibit curvatures, vehicle and shadow occlusions, roundabouts, and variational features. Experiments show that for roads with a certain width, completeness, and correctness of the method are more than 98%. Additionally, as compared with other algorithms, the interactive human intervention of this method is reduced by more than 2/3.

中文翻译：

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基于多描述符的高分辨率卫星图像道路提取

几何和纹理噪声使得道路图像规则难以准确描述，导致传统基于内部纹理均匀化的模板匹配算法自动化程度低。我们提出了一种基于多个描述符的半自动道路提取方法，以提高自动化程度，同时保证道路提取的准确性。该方法旨在解决道路图像几何信息不完整和道路内部纹理均匀性差的问题。建立了多尺度线段方向直方图模型和扇区描述符。通过插值和扩展跟踪道路点，并使用后处理来拟合跟踪点并提取道路路线。在本文中，不同类型、不同分辨率的高分辨率遥感影像，选择不同的场景，道路表现出曲率、车辆和阴影遮挡、环岛和变分特征。实验表明，对于一定宽度的道路，该方法的完整性和正确性均在98%以上。此外，与其他算法相比，该方法的交互人为干预减少了2/3以上。