Abstract
Road information as a type of basic geographic information is very important for services such as city planning and traffic navigation, as such there is an urgent need for updating road information in a timely manner. Scholars have proposed various methods of extracting roads from remote sensing images, but most of them are not applicable to rural roads with diverse materials, large curvature changes, and a severe shelter problem. In view of these problems, we propose a road extraction method based on geometric feature inference. In this method, we make full use of the linear characteristics of roads, and construct a geometric knowledge base of rural roads using information on selected sample road segments. Based on the knowledge base, we identify the parallel line pairs in images, and further conduct grouping and connection instructed by knowledge reasoning, and finally obtain complete rural roads. The case study in Xiangtan City of China’s Hunan Province validates the performance of the proposed method.
Highlights
Road information is important basic geographic information that is directly related to services such as city planning, traffic navigation, and disaster evacuation [1,2]
In order to solve the abovementioned challenges, this paper proposes a rural road extraction method based on geometric feature inference
The current road extraction methods based on remote sensing image have been widely studied, and scholars have made great progress for different types of regions, image data sources, and data types; little research has focused on rural road extraction
Summary
Road information is important basic geographic information that is directly related to services such as city planning, traffic navigation, and disaster evacuation [1,2]. In view of images with different resolutions, different data sources or roads of different grades, scholars have proposed a variety of road extraction methods or algorithms. They can be divided into four general categories: (1) methods based on spectral and geometric features. Some scholars extract road regions in the images according to geometric features of roads (in the form of lines or ribbons) and spectral features (spectrum of typical surface material) [6,7,8,9,10]; (2) methods based on multi-scale features. Heipke first proposed a multi-resolution approach for automatic road extraction from aerial images. The edges were grouped into parallel line pairs, and rules were used to combine results at two resolutions
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