Zimo Shen

The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent.

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%.