A Fast and Simple Method of Building Detection From Lidar Data Based on Scan Line Analysis

Hu, Xiangyun; Ye, Lizhi

doi:10.5194/isprsannals-ii-3-w1-7-2013

Cited by 17 publications

(13 citation statements)

References 18 publications

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“…Using profiles or scan lines of LiDAR data to segment a surface and classify objects is not new [3][4][5][6][7]. This study focuses on using cross-line elements for plane segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…An extended line segmentation of scanning line segmentation [7] is employed to segment the profiles in four directions (i.e., vertical, horizontal, upper right, and lower right). The angle between the split line segment and the horizon direction is calculated by using the Douglas-Peucker algorithm ( Figure 1) [47].…”

Section: Line Segmentationmentioning

confidence: 99%

“…Proper selection of the scan line direction is essential in these methods [4]. The profiles in one or more directions are utilized to segment the data in order to detect man-made structures (i.e., bridges and buildings) from the LiDAR point cloud [5][6][7]. These methods are usually effective and fast.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

2016

Remote Sensing

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Plane segmentation is an important step in feature extraction and 3D modeling from light detection and ranging (LiDAR) point cloud. The accuracy and speed of plane segmentation are two issues difficult to balance, particularly when dealing with a massive point cloud with millions of points. A fast and easy-to-implement algorithm of plane segmentation based on cross-line element growth (CLEG) is proposed in this study. The point cloud is converted into grid data. The points are segmented into line segments with the Douglas-Peucker algorithm. Each point is then assigned to a cross-line element (CLE) obtained by segmenting the points in the cross-directions. A CLE determines one plane, and this is the rationale of the algorithm. CLE growth and point growth are combined after selecting the seed CLE to obtain the segmented facets. The CLEG algorithm is validated by comparing it with popular methods, such as RANSAC, 3D Hough transformation, principal component analysis (PCA), iterative PCA, and a state-of-the-art global optimization-based algorithm. Experiments indicate that the CLEG algorithm runs much faster than the other algorithms. The method can produce accurate segmentation at a speed of 6 s per 3 million points. The proposed method also exhibits good accuracy.

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“…Using profiles or scan lines of LiDAR data to segment a surface and classify objects is not new [3][4][5][6][7]. This study focuses on using cross-line elements for plane segmentation.…”

Section: Discussionmentioning

confidence: 99%

Section: Line Segmentationmentioning

confidence: 99%

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Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

2016

Remote Sensing

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“… Linear methods, which analyze LiDAR cross-sections -strips and point lines (Hu and Ye, 2013), (Hosseini, 2014);  Planar methods (grid-based methods), which construct projection of LiDAR point cloud using either height parameter (Kodors et al, 2014), indexes like echo-rate and variance (Chehata et al, 2009), combination of pixel features extracted from LiDAR and hyperspectral images (Jahan and Awrangjeb, 2017);  Volume methods, which process filled and empty voxels (with and without points), (Wang et al, 2016), voxel features (Plaza-Leiva et al, 2017). The requirement "to satisfy regular point distribution" is mentioned in USGS "LiDAR Base Specification": "The spatial distribution of geometrically usable points will be uniform and regular.…”

Section: Point Density and Point Spacingmentioning

confidence: 99%

Point Distribution as True Quality of LiDAR Point Cloud

Kodors¹

2017

BJMC

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Abstract.A parameter "point density" is often used to evaluate the quality of aerial laser scanning data. It is a parameter simple for understanding and human imagination. However, the true quality of LiDAR point cloud is based on point distribution. There are researches, which mention importance of point distribution and users' false perception, that higher point density is better quality of LiDAR point cloud. The goal of this study is to define the mathematical model how to measure quality of LiDAR point cloud. This article discusses the point distribution and LiDAR data quality defining the image resolution of point cloud. It can be interesting for experts in civil geospatial intelligence, LiDAR data processing and flight planning.

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“…In their work, planar objects, like building, road are extracted as straight line segment and free form objects, such as tree were extracted as small line segment or irregular points. Hu & Ye (2013) used Douglas-Peucker algorithm to segment the ALS scan line into line segment and classified them into buildings and vegetation based on local analysis using simple rules.…”

Section: Related Workmentioning

confidence: 99%

Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field

Luo

Sohn

2013

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

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ABSTRACT:This paper describes a line-based classification method, which labels TLS point clouds into vertical object, ground, tree and low objects. A local classifier implements labeling task on individual site independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. In this paper, we describe an approach using contextual information as postclassification improvement to a local generative classifier. The contextual information is expected to compensate for ambiguity in objects' visual appearance. A generative classifier is produced using Gaussian Mixture Model (GMM), model parameters of which are iteratively optimized with Expectation-Maximization (EM). The model we use to incorporate contextual information is the Conditional Random Field (CRF), which improves the classification results obtained from GMM-EM classifier by incorporating neighborhood interactions among labeled objects as well as local appearance. The proposed method was validated with three TLS datasets acquired from RIEGL LMS-Z390i scanner using cross validation.

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A Fast and Simple Method of Building Detection From Lidar Data Based on Scan Line Analysis

Cited by 17 publications

References 18 publications

Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Point Distribution as True Quality of LiDAR Point Cloud

Line-based Classification of Terrestrial Laser Scanning Data using Conditional Random Field

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