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Open AccessArticle

Lane-Level Road Extraction from High-Resolution Optical Satellite Images

School of Geomatics, Liaoning Technical University, Fuxin 12300, China
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Remote Sens. 2019, 11(22), 2672; https://doi.org/10.3390/rs11222672
Received: 24 October 2019 / Revised: 14 November 2019 / Accepted: 14 November 2019 / Published: 15 November 2019
High-quality updates of road information play an important role in smart city planning, sustainable urban expansion, vehicle management, urban planning, traffic navigation, public health and other fields. However, due to interference from road geometry and texture noise, it is difficult to avoid the decline of automation while accurately extracting roads. Therefore, we propose a high-resolution optical satellite image lane-level road extraction method. First, from the perspective of template matching and considering road characteristics and relevant semantic relations, an adaptive correction model, an MLSOH (multi-scale line segment orientation histogram) descriptor, a sector descriptor, and a multiangle beamlet descriptor are proposed to solve the interference from geometry and texture noise in road template matching and tracking. Second, based on refined lane-level tracking, single-lane and double-lane road-tracking modes are designed to extract single-lane and double-lane roads, respectively. In this paper, Pleiades satellite and GF-2 images are selected to set up different scenarios for urban and rural areas. Experiments are carried out on the phenomena that restrict road extraction, such as tree occlusion, building shadow occlusion, road bending, and road boundary blurring. Compared with other methods, the proposed method not only ensures the accuracy of lane-level road extraction but also greatly improves the automation of road extraction. View Full-Text
Keywords: single point; single-lane; double-lane; template matching; high resolution single point; single-lane; double-lane; template matching; high resolution
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MDPI and ACS Style

Dai, J.; Zhu, T.; Zhang, Y.; Ma, R.; Li, W. Lane-Level Road Extraction from High-Resolution Optical Satellite Images. Remote Sens. 2019, 11, 2672. https://doi.org/10.3390/rs11222672

AMA Style

Dai J, Zhu T, Zhang Y, Ma R, Li W. Lane-Level Road Extraction from High-Resolution Optical Satellite Images. Remote Sensing. 2019; 11(22):2672. https://doi.org/10.3390/rs11222672

Chicago/Turabian Style

Dai, Jiguang; Zhu, Tingting; Zhang, Yilei; Ma, Rongchen; Li, Wantong. 2019. "Lane-Level Road Extraction from High-Resolution Optical Satellite Images" Remote Sens. 11, no. 22: 2672. https://doi.org/10.3390/rs11222672

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