scholarly journals Feature Extraction from High-density Point Clouds: Toward Automation of an Intelligent 3D Contactless Digitizing Strategy

2010 ◽  
Vol 7 (6) ◽  
pp. 863-874 ◽  
Author(s):  
C. Mehdi-Souzani ◽  
J. Digne ◽  
N. Audfray ◽  
C. Lartigue ◽  
J.-M. Morel
Keyword(s):  
Feature Extraction ◽  
Point Clouds ◽  
High Density ◽  
Density Point

scholarly journals Comparing Two Photo-Reconstruction Methods to Produce High Density Point Clouds and DEMs in the Corral del Veleta Rock Glacier (Sierra Nevada, Spain)

2014 ◽  
Vol 6 (6) ◽  
pp. 5407-5427 ◽  
Author(s):  
Álvaro Gómez-Gutiérrez ◽  
José de Sanjosé-Blasco ◽  
Javier de Matías-Bejarano ◽  
Fernando Berenguer-Sempere
Keyword(s):  
Sierra Nevada ◽  
Point Clouds ◽  
High Density ◽  
Density Point ◽  
Rock Glacier ◽  
Reconstruction Methods

Geomorphological mapping of an alpine rock glacier with multi-temporal UAV-based high density point cloud comparison

2020 ◽  
Author(s):  
Francesca Bearzot ◽  
Roberto Garzonio ◽  
Biagio Di Mauro ◽  
Umberto Morra Di Cella ◽  
Edoardo Cremonese ◽  
...  
Keyword(s):  
Surface Displacement ◽  
Point Clouds ◽  
Horizontal Displacement ◽  
Multiscale Model ◽  
Climatic Conditions ◽  
High Density ◽  
The Body ◽  
Density Point ◽  
Differential Gps ◽  
Rock Glacier

<p>The acquisition of high-resolution topographic data is a widely used tool for studies related to the processes and dynamics of the Earth's surface. In this work, we present the results of the repeated acquisition of photogrammetric data by Unmanned Aerial Vehicle (UAV) in order to detect the topographic evolution of an alpine rock glaciers located in Valtournenche (AO, Italy). Field monitoring conducted in recent years has shown significant variations in the behaviour of these landforms, with an increasing trend of their dynamism, raising questions about their stability in changing climatic conditions.</p><p> </p><p>The photogrammetric shots were taken with a DJ Phantom 4 UAV equipped with a compact RGB digital camera. The acquisitions were performed yearly from 2012 up to 2019 with a ground sampling distance never exceeding 5 cm/px. Contemporary to the acquisitions, approximately 20 Ground Control Points were placed on the rock glacier and on the surrounding areas and their coordinates were measured with a differential GPS (dGPS) for georeferencing UAV images. Moreover, in 2014, 2015 and 2019 geophysical campaigns were carried out for the detection of ice lenses under the debris cover of the rock glacier.</p><p> </p><p>Structure-from-motion techniques were applied on overlapping images to create high-density point clouds, than converted in orthophotos and digital surface models of the Earth’s surface.</p><p>The point clouds were analysed using the M3C2 (Multiscale Model to Model Cloud Comparison) plug-in, freely available in the CloudCompare software. Maps of surface changes between acquisition pairs in the period from 2015-2019 have been created. The comparison allowed the identification of "material supply" and "material removal" zones, slightly variable from one year to the next. The major accumulation zones are concentrated along the frontal sector of the rock glacier, more focused on the western sector (black lobe) and secondly on the right side of the rock glacier (white lobe). The removal of material is mainly concentrated on the higher altitude of the body but also in correspondence to the systems of crevasses and scarps and on the central part of the black lobe.</p><p>The surface displacement analysis of the rock glacier was also performed selecting manually several clearly identifiable features on the orthomosaics collected. Blocks and ridges-and-furrows complex were marked on the 2019 orthomosaic and found them on the 2015 orthomosaic. This approach allows improving and quantifying the dynamics of the different portions of the individual apparatus.</p><p>The velocity fields’ patterns highlight non-homogeneous displacements between the West (black lobe) and East part (white lobe) of the whole rock glacier. Specifically, the black lobe showed an average horizontal displacement of around 1 m/y while the white lobe moved significantly slower than the previous one (approximately 0.5 m/y). Overall, the rock glacier moved downslope at an average horizontal velocity of 0.60 m/y in the frontal tongue, 0.48 m/y in the central portion and 0.30 m/y in the upper zone.</p>

scholarly journals Registro tridimensional acumulativo de la secuencia estratigráfica. Fotogrametría y SIG en la intervención arqueológica de lo Boligni (Alacant)

2012 ◽  
Vol 3 (5) ◽  
pp. 81 ◽  
Author(s):  
Ana Mª Charquero Ballester ◽  
Jordi A. López Lillo
Keyword(s):  
Point Clouds ◽  
Archaeological Site ◽  
High Density ◽  
Data Recovery ◽  
Digital Tools ◽  
Massive Data ◽  
Density Point ◽  
The Everyday ◽  
Interfacial Surfaces ◽  
The Way

<p>In this paper a stratigraphic recording methodology is presented after a practial experience at the lo Boligni archaeological site. This experience has allowed, on the basis of cenital stereo-photo pairs, the digitalization of interfacial surfaces through high density point clouds renderization as the excavation went on, in order to obtain a Three-dimmensional Cummulative Model (TCM) of the stratigraphical sequence. It will be exposed the way some easy and affordable digital tools are used at the excavation in order to achieve a massive data recovery, and how their integration in CAD and GIS environments opens new possibilities for the everyday archaeological information treatement.</p>

Statistical process monitoring approach for high-density point clouds

2012 ◽  
Vol 24 (6) ◽  
pp. 1267-1279 ◽  
Author(s):  
Lee J. Wells ◽  
Fadel M. Megahed ◽  
Cory B. Niziolek ◽  
Jaime A. Camelio ◽  
William H. Woodall
Keyword(s):  
Process Monitoring ◽  
Point Clouds ◽  
High Density ◽  
Density Point ◽  
Statistical Process ◽  
Statistical Process Monitoring

Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations

2018 ◽  
Vol 39 (15-16) ◽  
pp. 5211-5235 ◽  
Author(s):  
Juan Guerra-Hernández ◽  
Diogo N. Cosenza ◽  
Luiz Carlos Estraviz Rodriguez ◽  
Margarida Silva ◽  
Margarida Tomé ◽  
...  
Keyword(s):  
Point Clouds ◽  
High Density ◽  
Density Point ◽  
Individual Tree ◽  
Tree Detection ◽  
Eucalyptus Plantations

scholarly journals A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

2021 ◽  
Vol 13 (8) ◽  
pp. 1442
Author(s):  
Kaisen Ma ◽  
Yujiu Xiong ◽  
Fugen Jiang ◽  
Song Chen ◽  
Hua Sun
Keyword(s):  
Point Cloud ◽  
Local Maximum ◽  
Window Size ◽  
Point Clouds ◽  
High Density ◽  
Watershed Algorithm ◽  
Canopy Area ◽  
Individual Trees ◽  
Optimal Window ◽  
Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

scholarly journals An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

2018 ◽  
Vol 7 (10) ◽  
pp. 409 ◽  
Author(s):  
Youqiang Dong ◽  
Ximin Cui ◽  
Li Zhang ◽  
Haibin Ai
Keyword(s):  
Point Clouds ◽  
High Density ◽  
Type I ◽  
Densification Process ◽  
Type I Errors ◽  
Second Stage ◽  
Standard Variance ◽  
Triangular Irregular Network ◽  
Two Stages ◽  
Type Ii Errors

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.

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