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Open AccessArticle

Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

by 1,2, 1, 3,* and 3
1
Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing 100871, China
2
China Transport Telecommunications & Information Centre, Ministry of Transport, Beijing 100029, China
3
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(10), 314; https://doi.org/10.3390/ijgi6100314
Received: 22 August 2017 / Revised: 5 October 2017 / Accepted: 14 October 2017 / Published: 19 October 2017
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. View Full-Text
Keywords: rural road extraction; remote sensing; geometric feature; knowledge inference rural road extraction; remote sensing; geometric feature; knowledge inference
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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.

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