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Special Issue "Airborne Laser Scanning"

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

Deadline for manuscript submissions: closed (21 October 2016)

Special Issue Editors

Guest Editor
Prof. Jie Shan

Purdue University, West Lafayette, IN, USA
Website | E-Mail
Interests: Automated aerospace image and LiDAR mapping; Geospatial modeling and analysis; Geosocial data mining
Guest Editor
Prof. Dr. Juha Hyyppä

Finish Geospatial Research Institute, Masala, Finland
Website | E-Mail
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests

Special Issue Information

Dear Colleagues,

Airborne laser scanning has recently embraced a revolution in technological advancements and various innovations in practical applications. Among numerous developments, we have notably experienced progressive changes from discrete recording to waveform recording, from single spectral (band) to multispectral laser scanning, and from traditional single pulse collection to multi pulse (Geiger mode) and single photon collections. Additionally, UAV laser scanning is emerging. As a result of such technological advancements, the operational platform altitude may vary from tens of meters to over ten-thousand meters; the point density reaches from a couple of points per square meter to tens or even a hundred points per square meter; additionally, the recorded targets can be air, canopy, ground, or even below water. Representative applications of such advancements include, among others, air quality detection, biophysical estimation, high definition terrain generation, topographic mapping, coastal mapping, change detection, and various types of 3D modeling.

As Guest Editors, we would like to dedicate this Special Issue to timely documenting these revolutionary developments and innovative applications. Well-prepared, unpublished submissions that address one or more of the following topics on airborne laser scanning are solicited:

  • Advances in laser scanning systems
  • Accurate direct sensor geo-referencing
  • Point cloud generation from LiDAR measurements
  • Filtering, segmentation, clustering and classification of LiDAR point clouds
  • Feature or object extraction and reconstruction from LiDAR point clouds
  • Combined use of laser scanning data and other geospatial data
  • Feasibility studies with new systems and sensors
  • New applications

Prof. Jie Shan
Prof. Juha Hyyppä
Guest Editors

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 papers will be 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 1800 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.

Keywords

  • Airborne LiDAR
  • Airborne Laser Scanning
  • Point clouds
  • Direct georeferencing
  • Clustering and classification
  • Segmentation

Published Papers (15 papers)

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Research

Open AccessArticle Multi-Feature Registration of Point Clouds
Remote Sens. 2017, 9(3), 281; https://doi.org/10.3390/rs9030281
Received: 23 October 2016 / Revised: 31 December 2016 / Accepted: 12 March 2017 / Published: 16 March 2017
Cited by 2 | PDF Full-text (21646 KB) | HTML Full-text | XML Full-text
Abstract
Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud [...] Read more.
Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space
Remote Sens. 2017, 9(1), 14; https://doi.org/10.3390/rs9010014
Received: 17 October 2016 / Revised: 29 November 2016 / Accepted: 22 December 2016 / Published: 27 December 2016
Cited by 10 | PDF Full-text (13953 KB) | HTML Full-text | XML Full-text
Abstract
Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D [...] Read more.
Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessFeature PaperArticle Scanning, Multibeam, Single Photon Lidars for Rapid, Large Scale, High Resolution, Topographic and Bathymetric Mapping
Remote Sens. 2016, 8(11), 958; https://doi.org/10.3390/rs8110958
Received: 29 July 2016 / Revised: 19 October 2016 / Accepted: 10 November 2016 / Published: 18 November 2016
Cited by 8 | PDF Full-text (31180 KB) | HTML Full-text | XML Full-text
Abstract
Several scanning, single photon sensitive, 3D imaging lidars are herein described that operate at aircraft above ground levels (AGLs) between 1 and 11 km, and speeds in excess of 200 knots. With 100 beamlets and laser fire rates up to 60 kHz, we, [...] Read more.
Several scanning, single photon sensitive, 3D imaging lidars are herein described that operate at aircraft above ground levels (AGLs) between 1 and 11 km, and speeds in excess of 200 knots. With 100 beamlets and laser fire rates up to 60 kHz, we, at the Sigma Space Corporation (Lanham, MD, USA), have interrogated up to 6 million ground pixels per second, all of which can record multiple returns from volumetric scatterers such as tree canopies. High range resolution has been achieved through the use of subnanosecond laser pulsewidths, detectors and timing receivers. The systems are presently being deployed on a variety of aircraft to demonstrate their utility in multiple applications including large scale surveying, bathymetry, forestry, etc. Efficient noise filters, suitable for near realtime imaging, have been shown to effectively eliminate the solar background during daytime operations. Geolocation elevation errors measured to date are at the subdecimeter level. Key differences between our Single Photon Lidars, and competing Geiger Mode lidars are also discussed. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar
Remote Sens. 2016, 8(11), 936; https://doi.org/10.3390/rs8110936
Received: 17 August 2016 / Revised: 20 October 2016 / Accepted: 3 November 2016 / Published: 10 November 2016
Cited by 29 | PDF Full-text (8872 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, [...] Read more.
In this paper we present a description of a new multispectral airborne mapping light detection and ranging (lidar) along with performance results obtained from two years of data collection and test campaigns. The Titan multiwave lidar is manufactured by Teledyne Optech Inc. (Toronto, ON, Canada) and emits laser pulses in the 1550, 1064 and 532 nm wavelengths simultaneously through a single oscillating mirror scanner at pulse repetition frequencies (PRF) that range from 50 to 300 kHz per wavelength (max combined PRF of 900 kHz). The Titan system can perform simultaneous mapping in terrestrial and very shallow water environments and its multispectral capability enables new applications, such as the production of false color active imagery derived from the lidar return intensities and the automated classification of target and land covers. Field tests and mapping projects performed over the past two years demonstrate capabilities to classify five land covers in urban environments with an accuracy of 90%, map bathymetry under more than 15 m of water, and map thick vegetation canopies at sub-meter vertical resolutions. In addition to its multispectral and performance characteristics, the Titan system is designed with several redundancies and diversity schemes that have proven to be beneficial for both operations and the improvement of data quality. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar
Remote Sens. 2016, 8(9), 771; https://doi.org/10.3390/rs8090771
Received: 27 June 2016 / Revised: 6 September 2016 / Accepted: 12 September 2016 / Published: 19 September 2016
Cited by 8 | PDF Full-text (2182 KB) | HTML Full-text | XML Full-text
Abstract
Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength [...] Read more.
Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessFeature PaperArticle Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program
Remote Sens. 2016, 8(9), 767; https://doi.org/10.3390/rs8090767
Received: 28 June 2016 / Revised: 19 August 2016 / Accepted: 8 September 2016 / Published: 19 September 2016
Cited by 12 | PDF Full-text (6526 KB) | HTML Full-text | XML Full-text
Abstract
Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these [...] Read more.
Data acquired by Harris Corporation’s (Melbourne, FL, USA) Geiger-mode IntelliEarth™ sensor and Sigma Space Corporation’s (Lanham-Seabrook, MD, USA) Single Photon HRQLS sensor were evaluated and compared to accepted 3D Elevation Program (3DEP) data and survey ground control to assess the suitability of these new technologies for the 3DEP. While not able to collect data currently to meet USGS lidar base specification, this is partially due to the fact that the specification was written for linear-mode systems specifically. With little effort on part of the manufacturers of the new lidar systems and the USGS Lidar specifications team, data from these systems could soon serve the 3DEP program and its users. Many of the shortcomings noted in this study have been reported to have been corrected or improved upon in the next generation sensors. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud
Remote Sens. 2016, 8(9), 730; https://doi.org/10.3390/rs8090730
Received: 20 May 2016 / Revised: 21 August 2016 / Accepted: 29 August 2016 / Published: 5 September 2016
Cited by 14 | PDF Full-text (13280 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the [...] Read more.
Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Edge Detection and Feature Line Tracing in 3D-Point Clouds by Analyzing Geometric Properties of Neighborhoods
Remote Sens. 2016, 8(9), 710; https://doi.org/10.3390/rs8090710
Received: 10 March 2016 / Revised: 23 August 2016 / Accepted: 24 August 2016 / Published: 1 September 2016
Cited by 13 | PDF Full-text (9232 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In [...] Read more.
This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Detecting Terrain Stoniness From Airborne Laser Scanning Data †
Remote Sens. 2016, 8(9), 720; https://doi.org/10.3390/rs8090720
Received: 29 June 2016 / Revised: 7 August 2016 / Accepted: 17 August 2016 / Published: 31 August 2016
Cited by 3 | PDF Full-text (3573 KB) | HTML Full-text | XML Full-text
Abstract
Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on [...] Read more.
Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve A U C = 0 . 74 and A U C = 0 . 85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data
Remote Sens. 2016, 8(8), 648; https://doi.org/10.3390/rs8080648
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 3 August 2016 / Published: 8 August 2016
Cited by 7 | PDF Full-text (3194 KB) | HTML Full-text | XML Full-text
Abstract
Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving [...] Read more.
Full-waveform lidar systems capture the complete backscattered signal from the interaction of the laser beam with targets located within the laser footprint. The resulting data have advantages over discrete return lidar, including higher accuracy of the range measurements and the possibility of retrieving additional returns from weak and overlapping pulses. In addition, radiometric characteristics of targets, e.g., target cross-section, can also be retrieved from the waveforms. However, waveform restoration and removal of the effect of the emitted system pulse from the returned waveform are critical for precise range measurement, 3D reconstruction and target cross-section extraction. In this paper, a sparsity-constrained regularization approach for deconvolution of the returned lidar waveform and restoration of the target cross-section is presented. Primal-dual interior point methods are exploited to solve the resulting nonlinear convex optimization problem. The optimal regularization parameter is determined based on the L-curve method, which provides high consistency in varied conditions. Quantitative evaluation and visual assessment of results show the superior performance of the proposed regularization approach in both removal of the effect of the system waveform and reconstruction of the target cross-section as compared to other prominent deconvolution approaches. This demonstrates the potential of the proposed approach for improving the accuracy of both range measurements and geophysical attribute retrieval. The feasibility and consistency of the presented approach in the processing of a variety of lidar data acquired under different system configurations is also highlighted. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics
Remote Sens. 2016, 8(7), 582; https://doi.org/10.3390/rs8070582
Received: 31 May 2016 / Revised: 1 July 2016 / Accepted: 5 July 2016 / Published: 9 July 2016
Cited by 6 | PDF Full-text (1154 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on [...] Read more.
Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R2) of up to 0.63–0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation
Remote Sens. 2016, 8(6), 501; https://doi.org/10.3390/rs8060501
Received: 13 March 2016 / Revised: 19 May 2016 / Accepted: 3 June 2016 / Published: 15 June 2016
Cited by 53 | PDF Full-text (4589 KB) | HTML Full-text | XML Full-text
Abstract
Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high [...] Read more.
Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Three-Dimensional Reconstruction of Building Roofs from Airborne LiDAR Data Based on a Layer Connection and Smoothness Strategy
Remote Sens. 2016, 8(5), 415; https://doi.org/10.3390/rs8050415
Received: 17 January 2016 / Revised: 29 March 2016 / Accepted: 12 April 2016 / Published: 16 May 2016
Cited by 3 | PDF Full-text (12875 KB) | HTML Full-text | XML Full-text
Abstract
A new approach for three-dimensional (3-D) reconstruction of building roofs from airborne light detection and ranging (LiDAR) data is proposed, and it includes four steps. Building roof points are first extracted from LiDAR data by using the reversed iterative mathematic morphological (RIMM) algorithm [...] Read more.
A new approach for three-dimensional (3-D) reconstruction of building roofs from airborne light detection and ranging (LiDAR) data is proposed, and it includes four steps. Building roof points are first extracted from LiDAR data by using the reversed iterative mathematic morphological (RIMM) algorithm and the density-based method. The corresponding relations between points and rooftop patches are then established through a smoothness strategy involving “seed point selection, patch growth, and patch smoothing.” Layer-connection points are then generated to represent a layer in the horizontal direction and to connect different layers in the vertical direction. Finally, by connecting neighboring layer-connection points, building models are constructed with the second level of detailed data. The key contributions of this approach are the use of layer-connection points and the smoothness strategy for building model reconstruction. Experimental results are analyzed from several aspects, namely, the correctness and completeness, deviation analysis of the reconstructed building roofs, and the influence of elevation to 3-D roof reconstruction. In the two experimental regions used in this paper, the completeness and correctness of the reconstructed rooftop patches were about 90% and 95%, respectively. For the deviation accuracy, the average deviation distance and standard deviation in the best case were 0.05 m and 0.18 m, respectively; and those in the worst case were 0.12 m and 0.25 m. The experimental results demonstrated promising correctness, completeness, and deviation accuracy with satisfactory 3-D building roof models. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Detection and Segmentation of Small Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning
Remote Sens. 2016, 8(5), 407; https://doi.org/10.3390/rs8050407
Received: 10 December 2015 / Revised: 28 April 2016 / Accepted: 4 May 2016 / Published: 11 May 2016
Cited by 5 | PDF Full-text (4232 KB) | HTML Full-text | XML Full-text
Abstract
Due to expected climate change and increased focus on forests as a potential carbon sink, it is of interest to map and monitor even marginal forests where trees exist close to their tolerance limits, such as small pioneer trees in the forest-tundra ecotone. [...] Read more.
Due to expected climate change and increased focus on forests as a potential carbon sink, it is of interest to map and monitor even marginal forests where trees exist close to their tolerance limits, such as small pioneer trees in the forest-tundra ecotone. Such small trees might indicate tree line migrations and expansion of the forests into treeless areas. Airborne laser scanning (ALS) has been suggested and tested as a tool for this purpose and in the present study a novel procedure for identification and segmentation of small trees is proposed. The study was carried out in the Rollag municipality in southeastern Norway, where ALS data and field measurements of individual trees were acquired. The point density of the ALS data was eight points per m2, and the field tree heights ranged from 0.04 to 6.3 m, with a mean of 1.4 m. The proposed method is based on an allometric model relating field-measured tree height to crown diameter, and another model relating field-measured tree height to ALS-derived height. These models are calibrated with local field data. Using these simple models, every positive above-ground height derived from the ALS data can be related to a crown diameter, and by assuming a circular crown shape, this crown diameter can be extended to a crown segment. Applying this model to all ALS echoes with a positive above-ground height value yields an initial map of possible circular crown segments. The final crown segments were then derived by applying a set of simple rules to this initial “map” of segments. The resulting segments were validated by comparison with field-measured crown segments. Overall, 46% of the field-measured trees were successfully detected. The detection rate increased with tree size. For trees with height >3 m the detection rate was 80%. The relatively large detection errors were partly due to the inherent limitations in the ALS data; a substantial fraction of the smaller trees was hit by no or just a few laser pulses. This prevents reliable detection of changes at an individual tree level, but monitoring changes on an area level could be a possible application of the method. The results further showed that some variation must be expected when the method is used for repeated measurements, but no significant differences in the mean number of segmented trees were found over an intensively measured test area of 11.4 ha. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements
Remote Sens. 2016, 8(5), 383; https://doi.org/10.3390/rs8050383
Received: 25 February 2016 / Revised: 10 April 2016 / Accepted: 27 April 2016 / Published: 5 May 2016
Cited by 6 | PDF Full-text (17382 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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