The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 289–293, 2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 289–293, 2016

  09 Jun 2016

09 Jun 2016


Y. Li1, X. Hu2, H. Guan3, and P. Liu4 Y. Li et al.
  • 1School of Civil Engineering and Architecture, Nanchang University, 330031, Nanchang, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, 430079, Wuhan, China
  • 3School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, 210044, Nanjing, China
  • 4College of Urban & Environment Science, Tianjin Normal University, 300387, Tianjin, China

Keywords: Remote sensing, feature extraction, pattern recognition, LiDAR, Road detection

Abstract. The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for “Urban Classification and 3D Building Reconstruction” project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.