SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

Zhang, Jixian; Lin, Xiangguo; Ning, Xi-Jing

doi:10.3390/rs5083749

Cited by 222 publications

(132 citation statements)

References 31 publications

(21 reference statements)

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“…Generally, most building façade segments are very large and aligned vertically Jochem et al, 2011), and most vegetation segments are small (Zhang et al, 2013;Rutzinger et al, 2008) and scattered in 3D space Zhang et al, 2013).Two features about orientation and scatterness are selected to detect object segment herein. A method to calculate the orientation and the scatterness of a planar segment was proposed in (Zhang et al, 2013), where the scatterness is calculated based on the principal components analysis . Based on visual evaluation of the histograms of orientation and scatterness, two thresholds about orientation O and scatterness S for distinguishing the façade and nonfaced, vegetation and non-vegetation can be determined in a tryand-error way.…”

Section: Object Segments Detectionmentioning

confidence: 99%

“…Moreover, once a point cloud has been segmented, segment attributes can be collected to classify the segments (Vosselman et al, 2010). As a result, similar to object-based image analysis (Blaschke et al, 2010), a segment-based classification is more reliable than a point-based classification (Vosselman et al, 2010;Zhang et al, 2013;Rutzinger et al, 2008) for point cloud. Thus, a segment-based method for ground measurement detection from MLS point cloud is proposed herein.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud

Lin

Zhang

2015

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

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ABSTRACT:In most Mobile Laser Scanning (MLS) applications, filtering is a necessary step. In this paper, a segmentation-based filtering method is proposed for MLS point cloud, where a segment rather than an individual point is the basic processing unit. Particularly, the MLS point cloud in some blocks are clustered into segments by a surface growing algorithm, then the object segments are detected and removed. A segment-based filtering method is employed to detect the ground segments. Two MLS point cloud datasets are used to evaluate the proposed method. Experiments indicate that, compared with the classic progressive TIN (Triangulated Irregular Network) densification algorithm, the proposed method is capable of reducing the omission error, the commission error and total error by 3.62%, 7.87% and 5.54% on average, respectively.

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Section: Object Segments Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud

Lin

Zhang

2015

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

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“…However, they employ Markov Random Field (MRF) classifier to identify power lines from the linear segments based on segment height, slope and parallelism. Zhang et al [28] classifies urban features such as ground, buildings and power lines from ALS data using support vector machine-based classification method. Zhu and Hyyppa [29] extract power lines from ALS data.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Object Extraction from Electrical Substation Point Clouds

Arastounia

Lichti

2015

Remote Sensing

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Abstract:The reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated methodology to recognize key components of electrical substations from 3D LiDAR data acquired using terrestrial laser scanning. The proposed methodology includes six novel algorithms to recognize key components (fence, cables, circuit breakers, bushings and bus pipes) of electrical substations. Three datasets with different resolutions and configurations are used in this study. A Leica HDS 6100 laser scanner was used to acquire the first dataset and a Faro Focus 3D laser scanner was employed to collect the second and third datasets. The obtained results indicate that 178 and 171 out of 181 electrical substation elements were successfully recognized in the first and second dataset, respectively, and 183 out of 191 components were identified in the third dataset. The results also demonstrate that an average 97.8% accuracy and average 98.8% precision at the point cloud level can be achieved.

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“…In this study, the elevation histogram method [22] is first used to exclude evident outliers. The Delaunay triangulation [23][24][25] is then applied to determine the less evident outliers.…”

Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Pang

Wang³

et al. 2014

Remote Sensing

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Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

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SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

Cited by 222 publications

References 31 publications

Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud

Segmentation-Based Ground Points Detection from Mobile Laser Scanning Point Cloud

Automatic Object Extraction from Electrical Substation Point Clouds

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

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