Abstract
Abstract. Shoulder line is the significant line in hilly area of Loess Plateau in China, dividing the surface into positive and negative terrain (P-N terrains). Due to the point cloud vegetation removal methods of P-N terrains are different, there is an imperative need for shoulder line extraction. In this paper, we proposed an automatic shoulder line extraction method based on point cloud. The workflow is as below: (i) ground points were selected by using a grid filter in order to remove most of noisy points. (ii) Based on DEM interpolated by those ground points, slope was mapped and classified into two classes (P-N terrains), using Natural Break Classified method. (iii) The common boundary between two slopes is extracted as shoulder line candidate. (iv) Adjust the filter gird size and repeat step i-iii until the shoulder line candidate matches its real location. (v) Generate shoulder line of the whole area. Test area locates in Madigou, Jingbian County of Shaanxi Province, China. A total of 600 million points are acquired in the test area of 0.23km2, using Riegl VZ400 3D Laser Scanner in August 2014. Due to the limit Granted computing performance, the test area is divided into 60 blocks and 13 of them around the shoulder line were selected for filter grid size optimizing. The experiment result shows that the optimal filter grid size varies in diverse sample area, and a power function relation exists between filter grid size and point density. The optimal grid size was determined by above relation and shoulder lines of 60 blocks were then extracted. Comparing with the manual interpretation results, the accuracy of the whole result reaches 85%. This method can be applied to shoulder line extraction in hilly area, which is crucial for point cloud denoising and high accuracy DEM generation.
Highlights
Terrestrial laser scanners (TLS) provide detailed and highly accurate 3D data rapidly and efficiently (Axelsson 1999, R 2003, Bitelli, Dubbini et al 2004, Bornaz and Rinaudo 2004, Barnea and Filin 2013)
The main objectives of this paper are :1) to propose a new method to extract shoulder line based on point cloud for helping remove vegetation points and promise the correct surface modelling; 2) to realize the method automatically based on finding some law in point cloud data
This paper proposed a method to extract shoulder line based on point data
Summary
Terrestrial laser scanners (TLS) provide detailed and highly accurate 3D data rapidly and efficiently (Axelsson 1999, R 2003, Bitelli, Dubbini et al 2004, Bornaz and Rinaudo 2004, Barnea and Filin 2013). The massive point cloud data acquired by TLS record three-dimensional information, they contain many “noise” points due to great number of obstructions in natural environments. Every “noise” point mainly vegetation point leads to incorrect surface modelling and need to be filtered and cleaned before surface modelling. Many scholars have researched the de-noisy algorithms and methods (Kraus and Pfeifer 1998, Axelsson 1999, Bleyer and Gelautz 2004, Ding, Ping et al 2005, Filin and Pfeifer 2006, Jutzi and Stilla 2006, Biosca and Lerma 2008, Barnea and Filin 2013, Pirotti, Guarnieri et al 2013). Our experiments shows that these algorithms and methods have good effects except for steep slope. Ma et al (2013) proposed a vegetation filtering method of TLS point cloud (Ma and Li 2013). There are some drawbacks in the mentioned methods to meet the practical applications
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