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Open AccessArticle

A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
China Transport Telecommunications & Information Center, Beijing 100011, China
Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Academic Editors: Jamal Jokar Arsanjani, Ming-Hsiang (Ming) Tsou and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(7), 114;
Received: 24 March 2016 / Revised: 2 July 2016 / Accepted: 8 July 2016 / Published: 13 July 2016
(This article belongs to the Special Issue Big Data for Urban Informatics and Earth Observation)
PDF [5207 KB, uploaded 13 July 2016]


Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction. View Full-Text
Keywords: spatial texture; local Moran’s I; hypothesis model; verification model; road extraction spatial texture; local Moran’s I; hypothesis model; verification model; road extraction

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Wang, J.; Qin, Q.; Gao, Z.; Zhao, J.; Ye, X. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS Int. J. Geo-Inf. 2016, 5, 114.

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