Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference
AbstractRoad 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. View Full-Text
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Liu, J.; Qin, Q.; Li, J.; Li, Y. Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference. ISPRS Int. J. Geo-Inf. 2017, 6, 314.
Liu J, Qin Q, Li J, Li Y. Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference. ISPRS International Journal of Geo-Information. 2017; 6(10):314.Chicago/Turabian Style
Liu, Jian; Qin, Qiming; Li, Jun; Li, Yunpeng. 2017. "Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference." ISPRS Int. J. Geo-Inf. 6, no. 10: 314.
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