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Lidar/Laser Scanning in Urban Environments
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Lidar/Laser Scanning in Urban Environments

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2015) | Viewed by 171676

Special Issue Editor

Prof. Dr. Juha Hyyppä

E-Mail Website
Guest Editor
Finish Geospatial Research Institute, 02431 Masala, Finland
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Laser scanning (LS) is a surveying technique used for mapping topography, vegetation, urban areas, ice, infrastructure, and other targets of interest. The main component of laser scanning is the Lidar. The output of the laser scanner becomes a georeferenced point cloud of Lidar measurements (concerning, for example, intensity and possibly the waveform information of the returned light). In addition to Airborne Laser Scanning (ALS), there is an increasing interest in Terrestrial Laser Scanning (TLS), where the laser scanner is mounted on a tripod or even on a moving platform, i.e. Mobile Laser Scanning (MLS).

Annually, the LS business market is growing by 15 %. Laser scanning is also increasingly used for built environments, having applications in 3D information modeling of cities (including BIM), road and roadside mapping, powerline mapping, urban forests, autonomous driving, smart cities, cultural heritage, etc. (just to name a few). Large IT companies are also collecting roadside information using Lidar, but we do not yet see these detailed results publicly.

Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript of their latest innovative research results in lidar/laser scanning in urban environments. Also reviews and contributions are welcomed. Original and innovative contributions may be from, but not limited to:

  • New methods in information extraction, i.e., automated feature extraction and object recognition, from all kinds of laser or ranging data to those concerning urban/built environments
  • New applications and concepts using laser scanning for urban/built environments
  • Techniques for the fusion of ALS and MLS data with that of other sensors
  • Mobile laser scanning developments
  • Accuracy and performance evaluations
  • New urban lidar developments

Prof. Juha Hyyppä
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (20 papers)

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4522 KiB  
Article
An Improved Method for Power-Line Reconstruction from Point Cloud Data
by Bo Guo, Qingquan Li, Xianfeng Huang and Chisheng Wang
Remote Sens. 2016, 8(1), 36; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8010036 - 05 Jan 2016
Cited by 98 | Viewed by 9400
Abstract
This paper presents a robust algorithm to reconstruct power-lines using ALS technology. Point cloud data are automatically classified into five target classes before reconstruction. In order to improve upon the defaults of only using the local shape properties of a single power-line span [...] Read more.
This paper presents a robust algorithm to reconstruct power-lines using ALS technology. Point cloud data are automatically classified into five target classes before reconstruction. In order to improve upon the defaults of only using the local shape properties of a single power-line span in traditional methods, the distribution properties of power-line group between two neighbor pylons and contextual information of related pylon objects are used to improve the reconstruction results. First, the distribution properties of power-line sets are detected using a similarity detection method. Based on the probability of neighbor points belonging to the same span, a RANSAC rule based algorithm is then introduced to reconstruct power-lines through two important advancements: reliable initial parameters fitting and efficient candidate sample detection. Our experiments indicate that the proposed method is effective for reconstruction of power-lines from complex scenarios. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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12333 KiB  
Article
Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data
by Sudan Xu, George Vosselman and Sander Oude Elberink
Remote Sens. 2015, 7(12), 17051-17076; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71215867 - 17 Dec 2015
Cited by 33 | Viewed by 6418
Abstract
The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are [...] Read more.
The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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12490 KiB  
Article
Radiometric Block Adjustment for Multi-Strip Airborne Waveform Lidar Data
by Tee-Ann Teo and Hsien-Ming Wu
Remote Sens. 2015, 7(12), 16831-16848; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71215856 - 11 Dec 2015
Cited by 6 | Viewed by 5479
Abstract
The airborne lidar system has been shown to be an effective and reliable method for spatial data collection. Lidar records the coordinates of point and intensity, dependent on range, incident angle, reflectivity of object, atmospheric condition, and several external factors. To fully utilize [...] Read more.
The airborne lidar system has been shown to be an effective and reliable method for spatial data collection. Lidar records the coordinates of point and intensity, dependent on range, incident angle, reflectivity of object, atmospheric condition, and several external factors. To fully utilize the intensity of a lidar system, several researchers have proposed correction models from lidar equations. The radiometric correction models are divided into physically-oriented models and data-oriented models. The lidar acquisition often contains multiple flight lines, and the radiation energy of each flight line can be calibrated independently by calibration coefficient. However, the calibrated radiances in the overlapped area have slightly different measurements. These parameters should be implicitly taken into account if calibrating radiances back to reflectance using known calibration targets. This study used a single-strip physically-oriented model to obtain a backscattering coefficient and a data-oriented model to obtain corrected intensity. We then selected homogeneous tie regions in the overlapped areas, and the differences between strips were compensated by gain and offset parameters in multi-strip radiometric block adjustment. The results were evaluated by the radiometric differences. Nine strips were acquired by Rigel Q680i system, and the experimental results showed that the delta intensity and delta backscattering coefficient of tie regions were improved up to 60% after multi-strip block adjustment. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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4382 KiB  
Article
Automatic Object Extraction from Electrical Substation Point Clouds
by Mostafa Arastounia and Derek D. Lichti
Remote Sens. 2015, 7(11), 15605-15629; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115605 - 19 Nov 2015
Cited by 23 | Viewed by 11525
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 [...] Read more.
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 Focus3D 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. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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1814 KiB  
Article
Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data
by Mostafa Arastounia
Remote Sens. 2015, 7(11), 14916-14938; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71114916 - 06 Nov 2015
Cited by 81 | Viewed by 10504
Abstract
This study is aimed at developing automated methods to recognize railroad infrastructure from 3D LIDAR data. Railroad infrastructure includes rail tracks, contact cables, catenary cables, return current cables, masts, and cantilevers. The LIDAR dataset used in this study is acquired by placing an [...] Read more.
This study is aimed at developing automated methods to recognize railroad infrastructure from 3D LIDAR data. Railroad infrastructure includes rail tracks, contact cables, catenary cables, return current cables, masts, and cantilevers. The LIDAR dataset used in this study is acquired by placing an Optech Lynx mobile mapping system on a railcar, operating at 125 km/h. The acquired dataset covers 550 meters of Austrian rural railroad corridor comprising 31 railroad key elements and containing only spatial information. The proposed methodology recognizes key components of the railroad corridor based on their physical shape, geometrical properties, and the topological relationships among them. The developed algorithms managed to recognize all key components of the railroad infrastructure, including two rail tracks, thirteen masts, thirteen cantilevers, one contact cable, one catenary cable, and one return current cable. The results are presented and discussed both at object level and at point cloud level. The results indicate that 100% accuracy and 100% precision at the object level and an average of 96.4% accuracy and an average of 97.1% precision at point cloud level are achieved. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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8123 KiB  
Article
Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data
by Mohammad Awrangjeb
Remote Sens. 2015, 7(10), 14119-14150; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71014119 - 27 Oct 2015
Cited by 30 | Viewed by 8551
Abstract
Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support [...] Read more.
Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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1678 KiB  
Article
Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud
by Liang Cheng, Yang Wu, Lihua Tong, Yanming Chen and Manchun Li
Remote Sens. 2015, 7(10), 13921-13944; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71013921 - 23 Oct 2015
Cited by 22 | Viewed by 7139
Abstract
A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with [...] Read more.
A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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63312 KiB  
Article
Evaluation of a Backpack-Mounted 3D Mobile Scanning System
by Helge A. Lauterbach, Dorit Borrmann, Robin Heß, Daniel Eck, Klaus Schilling and Andreas Nüchter
Remote Sens. 2015, 7(10), 13753-13781; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71013753 - 21 Oct 2015
Cited by 64 | Viewed by 8499
Abstract
Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome [...] Read more.
Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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1887 KiB  
Article
Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm
by Borja Rodríguez-Cuenca, Silverio García-Cortés, Celestino Ordóñez and Maria C. Alonso
Remote Sens. 2015, 7(10), 12680-12703; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71012680 - 28 Sep 2015
Cited by 70 | Viewed by 10288
Abstract
Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial [...] Read more.
Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX) anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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967 KiB  
Article
Luminance-Corrected 3D Point Clouds for Road and Street Environments
by Matti T. Vaaja, Matti Kurkela, Juho-Pekka Virtanen, Mikko Maksimainen, Hannu Hyyppä, Juha Hyyppä and Eino Tetri
Remote Sens. 2015, 7(9), 11389-11402; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70911389 - 08 Sep 2015
Cited by 23 | Viewed by 7357
Abstract
A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. [...] Read more.
A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. A calculation of the luminance (cd/m2) was based on the RGB output values of a Nikon D800E digital still camera. The camera was calibrated with a reference luminance source. The relative orientation between the luminance images and intensity image of the 3D point cloud was solved in order to integrate the data sets into the same coordinate system. As a result, the 3D model of road environment luminance is illustrated and the ability to exploit the method for evaluating the luminance distribution on the road surface is presented. Furthermore, the limitations and future prospects of the methodology are addressed. The method provides promising results for studying road lighting conditions in future lighting optimizations. The paper presents the methodology and its experimental application on a road section which consists of five luminaires installed on one side of a two-lane road in Otaniemi, Espoo, Finland. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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1870 KiB  
Article
Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes
by Ting On Chan and Derek D. Lichti
Remote Sens. 2015, 7(8), 10480-10500; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70810480 - 17 Aug 2015
Cited by 21 | Viewed by 9100
Abstract
The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development [...] Read more.
The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development of a new automatic calibration method that allows quick and frequent recovery of the inherent time-varying errors. The basic principle of the method is that the LiDAR’s internal systematic error parameters are estimated by constraining point clouds of some known and automatically detected cylindrical features such as lamp poles to fit to the 3D cylinder models. This is analogous to the plumb-line calibration method in which the lens distortion parameters are estimated by constraining the image points of straight lines to fit to the 2D line model. The calibration can be performed at every measurement epoch in both static and kinematic modes. Four real datasets were used to verify the method, two of which were captured in static mode and the other two in kinematic mode. The overall results indicate that up to approximately 72% and 41% accuracy improvement were realized as a result of the calibration for the static and kinematic datasets, respectively. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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2054 KiB  
Article
Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data
by Hao Xu, Liang Cheng, Manchun Li, Yanming Chen and Lishan Zhong
Remote Sens. 2015, 7(8), 9682-9704; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70809682 - 30 Jul 2015
Cited by 40 | Viewed by 7811
Abstract
Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates [...] Read more.
Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered using the iterative closest point algorithm, and a progressive triangulated irregular network densification filtering algorithm is used to separate ground points from non-ground points. Second, an octree is generated from the non-ground points to store and index the irregularly-distributed LiDAR points. Finally, by comparing the LiDAR points from two dates and using the AutoClust algorithm, those areas of buildings and trees in the urban environment that have changed are determined effectively and efficiently. The key contributions of this approach are the development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas, enabling the use of the point-based method over large areas. To evaluate the proposed method, a series of experiments using aerial images are conducted. The results demonstrate that satisfactory performance can be obtained using the proposed approach. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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6716 KiB  
Article
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory
by Caiyun Zhang, Yuhong Zhou and Fang Qiu
Remote Sens. 2015, 7(6), 7892-7913; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70607892 - 16 Jun 2015
Cited by 119 | Viewed by 12653
Abstract
The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, [...] Read more.
The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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849 KiB  
Article
A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems
by Qingzhou Mao, Liang Zhang, Qingquan Li, Qingwu Hu, Jianwei Yu, Shaojun Feng, Washington Ochieng and Hanlu Gong
Remote Sens. 2015, 7(6), 7402-7424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70607402 - 03 Jun 2015
Cited by 19 | Viewed by 6827
Abstract
In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system [...] Read more.
In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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4708 KiB  
Article
Empirical Radiometric Normalization of Road Points from Terrestrial Mobile Lidar System
by Tee-Ann Teo and Hui-Lin Yu
Remote Sens. 2015, 7(5), 6336-6357; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70506336 - 21 May 2015
Cited by 20 | Viewed by 6146
Abstract
Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical [...] Read more.
Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS) is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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45551 KiB  
Article
Automatic Extraction of Railroad Centerlines from Mobile Laser Scanning Data
by Sander Oude Elberink and Kourosh Khoshelham
Remote Sens. 2015, 7(5), 5565-5583; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70505565 - 04 May 2015
Cited by 51 | Viewed by 9987
Abstract
In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser Scanning systems are able to capture the 3D environment of the rail tracks with a high level of detail. Our approach first detects laser points that were reflected by [...] Read more.
In this paper, we describe the automatic extraction of centerlines of railroads. Mobile Laser Scanning systems are able to capture the 3D environment of the rail tracks with a high level of detail. Our approach first detects laser points that were reflected by the rail tracks, by making use of local properties such as parallelism and height in relation to neighboring objects. In the modeling stage, we present two approaches to determine the centerline location. The first approach generates center points in a data-driven manner by projecting rail track points to the parallel track, and taking the midpoint as initial center point. Next, a piecewise linear function is fitted through the center points to generate center points at a regular interval. The second approach models the rail track by fitting piecewise 3D track models to the rail track points. The model consists of a pair of two parallel rail tracks. The fitted pieces are smoothened by a Fourier series interpolation function. After that the centerline is implicitly determined by the geometric center of the pair of tracks. Reference data has been used to analyze the quality of our results, confirming that the position of the centerlines can be determined with an accuracy of 2–3 cm. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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28546 KiB  
Article
Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data
by Liang Cheng, Lihua Tong, Yang Wu, Yanming Chen and Manchun Li
Remote Sens. 2015, 7(2), 1915-1936; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70201915 - 09 Feb 2015
Cited by 15 | Viewed by 6905
Abstract
A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an [...] Read more.
A new automated approach to the high-accuracy registration of airborne and terrestrial LiDAR data is proposed, which has three primary steps. Firstly, airborne and terrestrial LiDAR data are used to extract building corners, known as airborne corners and terrestrial corners, respectively. Secondly, an initial matching relationship between the terrestrial corners and airborne corners is automatically derived using a matching technique based on maximum matching corner pairs with minimum errors (MTMM). Finally, a set of leading points are generated from matched airborne corners, and a shiftable leading point method is proposed. The key feature of this approach is the implementation of the concept of shiftable leading points in the final step. Since the geometric accuracy of terrestrial LiDAR data is much better than that of airborne LiDAR data, leading points corresponding to anomalous airborne corners could be modified for the improvement of the geometric accuracy of registration. The experiment demonstrates that the proposed approach can advance the geometric accuracy of two-platform LiDAR data registration effectively. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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1764 KiB  
Article
Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas
by Lingli Zhu and Juha Hyyppä
Remote Sens. 2014, 6(11), 11267-11282; https://0-doi-org.brum.beds.ac.uk/10.3390/rs61111267 - 13 Nov 2014
Cited by 88 | Viewed by 10266
Abstract
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On [...] Read more.
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On the contrary, lower voltage power lines are located in the middle of dense forests, and it is difficult to classify power lines in such an environment. This paper proposes an automated power line detection method for forest environments. Our method was developed based on statistical analysis and 2D image-based processing technology. During the process of statistical analysis, a set of criteria (e.g., height criteria, density criteria and histogram thresholds) is applied for selecting the candidates for power lines. After transforming the candidates to a binary image, image-based processing technology is employed. Object geometric properties are considered as criteria for power line detection. This method was conducted in six sets of airborne laser scanning (ALS) data from different forest environments. By comparison with reference data, 93.26% of power line points were correctly classified. The advantages and disadvantages of the methods were analyzed and discussed. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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982 KiB  
Technical Note
A Model-Driven Approach for 3D Modeling of Pylon from Airborne LiDAR Data
by Qingquan Li, Zhipeng Chen and Qingwu Hu
Remote Sens. 2015, 7(9), 11501-11524; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70911501 - 09 Sep 2015
Cited by 29 | Viewed by 7809
Abstract
Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging) point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional [...] Read more.
Reconstructing three-dimensional model of the pylon from LiDAR (Light Detection And Ranging) point clouds automatically is one of the key techniques for facilities management GIS system of high-voltage nationwide transmission smart grid. This paper presents a model-driven three-dimensional pylon modeling (MD3DM) method using airborne LiDAR data. We start with constructing a parametric model of pylon, based on its actual structure and the characteristics of point clouds data. In this model, a pylon is divided into three parts: pylon legs, pylon body and pylon head. The modeling approach mainly consists of four steps. Firstly, point clouds of individual pylon are detected and segmented from massive high-voltage transmission corridor point clouds automatically. Secondly, an individual pylon is divided into three relatively simple parts in order to reconstruct different parts with different strategies. Its position and direction are extracted by contour analysis of the pylon body in this stage. Thirdly, the geometric features of the pylon head are extracted, from which the head type is derived with a SVM (Support Vector Machine) classifier. After that, the head is constructed by seeking corresponding model from pre-build model library. Finally, the body is modeled by fitting the point cloud to planes. Experiment results on several point clouds data sets from China Southern high-voltage nationwide transmission grid from Yunnan Province to Guangdong Province show that the proposed approach can achieve the goal of automatic three-dimensional modeling of the pylon effectively. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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2490 KiB  
Technical Note
Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models
by Xiangyun Hu, Lizhi Ye, Shiyan Pang and Jie Shan
Remote Sens. 2015, 7(8), 10996-11015; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70810996 - 24 Aug 2015
Cited by 25 | Viewed by 6947
Abstract
Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data [...] Read more.
Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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