Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

Roads play a vital role in rural economic development. However, rural roads feature irregular curvature changes, are narrow, and are built using diverse construction materials, which renders the analysis of road geometric and spectral characteristics less certain and reduces the automation ability of existing methods. Thus, this letter proposes a semiautomatic extraction of rural roads from high-resolution remote sensing images based on a multifeature combination. First, to address irregular curvature change characteristics of rural roads, we modify multiscale line segment orientation histogram (MLSOH) descriptors to reduce the impact of local curvature change on tracking. Second, we design a multicircle template to analyze the contrast between roads and nonroads and solve the problem of narrow roads. Last, we propose a panchromatic and hue, saturation, value (HSV) space interactive matching model to solve the problem of matching diverse road construction materials. This letter employs Pleiades satellite and GF-2 imagery. Compared with other methods, the proposed method improves automated road extraction by ensuring the extraction accuracy.