Segmentation of airborne laser scanning data using a slope adaptive neighborhood

Filin, Sagi; Pfeifer, Norbert

doi:10.1016/j.isprsjprs.2005.10.005

Cited by 204 publications

(111 citation statements)

References 4 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…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.…”

Section: Introductionmentioning

confidence: 68%

Point Cloud Oriented Shoulder Line Extraction in Loess Hilly Area

Yang

Xiong

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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 0.23km 2 , 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.

show abstract

Section: Introductionmentioning

confidence: 68%

Point Cloud Oriented Shoulder Line Extraction in Loess Hilly Area

Yang

Xiong

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Kim and Habib (2007) utilize cylinder neighbourhood concept for the segmentation of planar patches using parametric-domain methods successfully. Filin and Pfeifer (2006), Lari et al (2012) and Lari and Habib (2013) improve their planar surface segmentation results by considering the noise level and the physical shape of the associated surface. However, all mentioned papers take advantage of the parametric-domain methods that are computationally not efficient.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

Arastounia

Lichti

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:According to the Department of Energy of the USA, today's electrical distribution system is 97.97% reliable. However, power outages and interruptions still impact many people. Many power outages are caused by animals coming into contact with the conductive elements of the electrical substations. This can be prevented by covering the conductive electrical objects with insulating materials. The design of these custom-built insulating covers requires a 3D as-built plan of the substation. This research aims to develop automated methods to create such a 3D as-built plan using terrestrial LiDAR data for which objects first need to be recognized in the LiDAR point clouds. This paper reports on the application of a new algorithm for the segmentation of planar surfaces found at electrical substations. The proposed approach is a region growing method that aggregates points based on their proximity to each other and their neighbourhood dispersion direction. PCA (principal components analysis) is also employed to segment planar surfaces in the electrical substation. In this research two different laser scanners, Leica HDS 6100 and Faro Focus 3D , were utilized to scan an electrical substation in Airdrie, a city located in north of Calgary, Canada. In this research, three subsets incorporating one subset of Leica dataset with approximately 1.7 million points and two subsets of the Faro dataset with 587 and 79 thousand points were utilized. The performance of our proposed method is compared with the performance of PCA by performing check point analysis and investigation of computational speed. Both methods managed to detect a great proportion of planar points (about 70%). However, the proposed method slightly outperformed PCA. 95% of the points that were segmented by both methods as planar points did actually lie on a planar surface. This exhibits the high ability of both methods to identify planar points. The results also indicate that the computational speed of our method is superior to that of PCA by 50%. It is concluded that our proposed method achieves better results with higher computational speed than PCA in the segmentation of planar surfaces.

show abstract

“…Sampath and Shan (2010) analyze the eigenvalues in the voronoi neighborhood of the roof points and cluster the surface normals for segmentation. Filin and Pfeifer, (2006) also use feature clustering on 3D lidar point clouds with slope adaptive neighborhood. Biosca and Lerma, (2008) present an unsupervised fuzzy clustering based segmentation approach for TLS point clouds.…”

Section: Related Workmentioning

confidence: 99%

Min-Cut Based Segmentation of Airborne Lidar Point Clouds

Ural

Shan

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Introducing an organization to the unstructured point cloud before extracting information from airborne lidar data is common in many applications. Aggregating the points with similar features into segments in 3-D which comply with the nature of actual objects is affected by the neighborhood, scale, features and noise among other aspects. In this study, we present a min-cut based method for segmenting the point cloud. We first assess the neighborhood of each point in 3-D by investigating the local geometric and statistical properties of the candidates. Neighborhood selection is essential since point features are calculated within their local neighborhood. Following neighborhood determination, we calculate point features and determine the clusters in the feature space. We adapt a graph representation from image processing which is especially used in pixel labeling problems and establish it for the unstructured 3-D point clouds. The edges of the graph that are connecting the points with each other and nodes representing feature clusters hold the smoothness costs in the spatial domain and data costs in the feature domain. Smoothness costs ensure spatial coherence, while data costs control the consistency with the representative feature clusters. This graph representation formalizes the segmentation task as an energy minimization problem. It allows the implementation of an approximate solution by min-cuts for a global minimum of this NP hard minimization problem in low order polynomial time. We test our method with airborne lidar point cloud acquired with maximum planned post spacing of 1.4 m and a vertical accuracy 10.5 cm as RMSE. We present the effects of neighborhood and feature determination in the segmentation results and assess the accuracy and efficiency of the implemented min-cut algorithm as well as its sensitivity to the parameters of the smoothness and data cost functions. We find that smoothness cost that only considers simple distance parameter does not strongly conform to the natural structure of the points. Including shape information within the energy function by assigning costs based on the local properties may help to achieve a better representation for segmentation.

show abstract

Segmentation of airborne laser scanning data using a slope adaptive neighborhood

Cited by 204 publications

References 4 publications

Point Cloud Oriented Shoulder Line Extraction in Loess Hilly Area

Point Cloud Oriented Shoulder Line Extraction in Loess Hilly Area

Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood

Min-Cut Based Segmentation of Airborne Lidar Point Clouds

Contact Info

Product

Resources

About