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.
Highlights
Automatic road extraction from remotely sensed data has been an active research area during last few decades
Poullis (2014) proposed a novel framework for road extraction and classification from satellite images, which denoted as Tensor-Cuts
The subsequent steps include three major steps: (1) spatial clustering using adaptive mean shift for road center point detection, (2) local principal component analysis and least square line fitting for the primitives of road centerlines extraction, (3) hierarchical primitives grouping for connecting roads segments into complete roads network
Summary
Automatic road extraction from remotely sensed data has been an active research area during last few decades. (4)The intensity of LiDAR points can be used as an additional, useful feature for road extraction because road surfaces have similar reflectance. There is no point in the area occluded by tall objects To solve this problem, points are interpolated into intensity image (Zhao, 2012; Zhu, 2009), binary image (Clode, 2007), or others (Samadzadegan, 2009; Jiangui, 2011) to extract road lines. To deal with urban roads with various patterns and width, we develop a spatial clustering algorithm with adaptive window size base on mean shift, described in our previous paper (Hu, 2014) This method is robust to LiDAR points with non-uniform and irregular distribution, and does not require a prior road model and rasterization. Compared with MTH, this method greatly reduces computational cost, and achieves the same performance with MTH
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