Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
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Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong, China
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School of Geosciences & Info-Physics, Central South University, Changsha 410083, China
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Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410083, China
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School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 900; https://doi.org/10.3390/rs10060900
Received: 11 April 2018
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Revised: 19 May 2018
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Accepted: 31 May 2018
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Published: 7 June 2018
(This article belongs to the Special Issue Very High Resolution (VHR) Satellite Imagery: Processing and Applications)
In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements.
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Keywords:
road extraction; very high-resolution image; fast marching method; semiautomatic; edge constraint
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Gao, L.; Shi, W.; Miao, Z.; Lv, Z. Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images. Remote Sens. 2018, 10, 900. https://doi.org/10.3390/rs10060900
AMA Style
Gao L, Shi W, Miao Z, Lv Z. Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10(6):900. https://doi.org/10.3390/rs10060900
Chicago/Turabian StyleGao, Lipeng, Wenzhong Shi, Zelang Miao, and Zhiyong Lv. 2018. "Method Based on Edge Constraint and Fast Marching for Road Centerline Extraction from Very High-Resolution Remote Sensing Images" Remote Sensing 10, no. 6: 900. https://doi.org/10.3390/rs10060900
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