Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis

Wang, Yu; Cheng, Liang; Chen, Yanming; Wu, Yang; Li, Manchun

doi:10.3390/rs8050419

Cited by 43 publications

(40 citation statements)

References 47 publications

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“…A number of neighborhood optimizing methods (Guo et al, 2015;Mitra and Nguyen, 2003;Lalonde et al, 2005;Pauly et al, 2003;Belton and Lichti, 2006;Demantke et al, 2011;Weinmann et al, 2014) have been proposed. Unfortunately, these neighborhood optimization methods are rather time-consuming (Wang et al, 2016), which is the main disadvantage of this kind of classification strategy.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

Lin

Zhang

2017

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

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ABSTRACT:A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

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Section: Introductionmentioning

confidence: 99%

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

Lin

Zhang

2017

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

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“…Other examples are segment-based voxel methods (Wang et al, 2016) and grid-based methods (Kodors et al, 2014), which are sensitive to image resolution strongly related with point distribution too. If the distribution of points forms the dense groups, segmentation is not possible due to high number of holes in voxel cube or in 2D grid.…”

Section: Quality Meaning Among Different Expertsmentioning

confidence: 99%

“…If the distribution of points forms the dense groups, segmentation is not possible due to high number of holes in voxel cube or in 2D grid. The traditional methods to define the classification quality are error matrix/confusion matrix, total accuracy and Cohen's Kappa coefficient (Chehata et al, 2009), (Kodors et al, 2014), (Kodors and Kangro, 2016), (Wang et al, 2016).…”

Section: Quality Meaning Among Different Expertsmentioning

confidence: 99%

“… 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%

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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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“…1 ∑ | − | Wang et al, 2016;Yao et al, 2014) to evaluate the performance of object detection algorithms. These were calculated using the manually extracted segments as reference.…”

Section: = 100mentioning

confidence: 99%

Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data

Velumani¹,

Elberink²,

Yang³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxelbased segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85%, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94% and takes less than 20 seconds to process 50,000 points with an average point density of 16 points/cm 2 . Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.

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Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis

Cited by 43 publications

References 47 publications

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

Point Distribution as True Quality of LiDAR Point Cloud

Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data

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