Special Issue "Point Cloud Processing and Analysis in Remote Sensing"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Dr. Joaquín Martínez-Sánchez
Website1 Website2
Guest Editor
Applied Geotechnologies Research Group. Mining and Energy School. Maxwell AV. 36310 Vigo (Spain)
Interests: Remote Sensing; Photogrammetry; LiDAR; Image Processing; UAV
Dr. Higinio González Jorge
Website
Guest Editor
Universidad de Vigo, Department of Natural Resources and Environmental Engineering, School of Aerospace Engineering, University of Vigo, 32004, Ourense, Spain
Interests: LiDAR; unmanned aerial systems; UAV navigation; point cloud processing; GIS

Special Issue Information

Dear Colleagues,

With the popularization of photogrammetric image processing solutions and the decreasing costs of 3D laser scanners, point clouds have become one of the fundamental datasets for geomatics and remote sensing. As a result of dense image-matching techniques, point clouds have a great versatility to describe surfaces from microscopic elements to vast regions. At the same time, laser scanner systems integration in mobile, aerial, and satellite platforms can obtain billion-point geometric surveys of the environment along with radiometric information from the intensity attribute of the points at the wavelengths of the light source that may be complemented with the co-registration of image sensors.

The heterogeneity of unstructured point clouds in terms of precision and accuracy figures, intensity and radiometric information, or point density values, remains a challenge for point cloud processing and analysis in remote sensing of the environment.

This Remote Sensing Special Issue is meant to support the aforementioned scope by collecting and publishing research and review papers on, but not limited to, the following topics:

  • Data models and compression for huge dataset storage
  • Multiple sources point cloud registration and fusion
  • Out-of-core processing and visualization of large datasets
  • Multitemporal analysis of point cloud data
  • New approaches and methodologies for point cloud based semantics.
  • Environment change detection based on point clouds.
  • Multispectral point clouds acquisition and processing.
  • Remote Sensing applications of 3D modeling
  • Forest and asset inventory based on point clouds
  • Performance evaluation of point cloud processing and analysis methods.

We look forward to receiving your submissions in this challenging area.

Dr. Joaquín Martínez-Sánchez
Dr. Higinio González-Jorge
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 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 2000 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

  • LiDAR and photogrammetry
  • Surveying, acquisition and Data Models
  • Point cloud processing and registration
  • Point cloud analysis, segmentation, and classification
  • 3D Modeling and interoperability

Published Papers (7 papers)

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Research

Open AccessArticle
Remote Detection of Moisture and Bio-Deterioration of Building Walls by Time-Of-Flight and Phase-Shift Terrestrial Laser Scanners
Remote Sens. 2020, 12(11), 1708; https://doi.org/10.3390/rs12111708 - 27 May 2020
Abstract
Detection of bio-deterioration and moisture is one of the most important tasks for comprehensive diagnostic measurements of buildings and structures. Any undesirable change in the material properties caused by the action of biological agents contributes to gradual aesthetic and physical damage to buildings. [...] Read more.
Detection of bio-deterioration and moisture is one of the most important tasks for comprehensive diagnostic measurements of buildings and structures. Any undesirable change in the material properties caused by the action of biological agents contributes to gradual aesthetic and physical damage to buildings. Very often, such surface changes can lead to structural defects or poor maintenance. In this paper, radiometric analysis of point clouds is proposed for moisture and biofilm detection in building walls. Recent studies show that remote terrestrial laser scanning (TLS) technology is very useful for registering and evaluating the technical state of the deterioration of building walls caused by moisture and microorganisms. Two different types of TLS, time-of-flight and phase-shift scanners, were used in the study. The potential of TLS radiometric data for detecting moisture and biofilm on wall surfaces was tested on two buildings. The main aim of the research is to compare two types of scanners in the context of their use in the detection of moisture and microorganisms. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads
Remote Sens. 2020, 12(10), 1677; https://doi.org/10.3390/rs12101677 - 23 May 2020
Abstract
The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally [...] Read more.
The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds
Remote Sens. 2020, 12(7), 1224; https://doi.org/10.3390/rs12071224 - 10 Apr 2020
Abstract
The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration [...] Read more.
The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
Remote Sens. 2019, 11(22), 2600; https://doi.org/10.3390/rs11222600 - 06 Nov 2019
Cited by 1
Abstract
Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by [...] Read more.
Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a spatial hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
Application for Terrestrial LiDAR on Mudstone Erosion Caused by Typhoons
Remote Sens. 2019, 11(20), 2425; https://doi.org/10.3390/rs11202425 - 18 Oct 2019
Cited by 2
Abstract
Storms are important agents for shaping the Earth’s surface and often dominate the landscape evolution of mudstone areas, by rapid erosion and deposition. In our research, we used terrestrial scanning LiDAR (TLS) to detect surface changes in a 30 m in height, 60 [...] Read more.
Storms are important agents for shaping the Earth’s surface and often dominate the landscape evolution of mudstone areas, by rapid erosion and deposition. In our research, we used terrestrial scanning LiDAR (TLS) to detect surface changes in a 30 m in height, 60 m in width mudstone slope. This target slope shows the specific erosion pattern during extreme rainfall events such as typhoons. We investigate two major subjects: (1) how typhoon events impact erosion in the target slope, and (2) how rills develop on the hillslopes during these observation periods. There were three scans obtained in 2011, and converted to two observation periods. The permanent target points (TP) method and DEMs of differences were used to check the accuracy of point cloud. The results showed that the average erosion rate was 5 cm during the dry period in 2011. Following the typhoons, the erosion rate increased 1.4 times to 7 cm and was better correlated with the increase in the rainfall intensity than with general precipitation amounts. The hillslope gradient combined with rainfall intensity played a significant role in the geomorphic process. We found that in areas with over 75° gradients with larger rainfall intensity showed more erosion that at other gradients. The gradient also influenced the rill development, which occurred at middle and low gradients but not at high gradients. The rills also created a transition zone for erosion and deposition at the middle gradient where a minimal change occurred. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
From BIM to Scan Planning and Optimization for Construction Control
Remote Sens. 2019, 11(17), 1963; https://doi.org/10.3390/rs11171963 - 21 Aug 2019
Cited by 2
Abstract
Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) [...] Read more.
Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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Open AccessArticle
Structure from Motion Point Clouds for Structural Monitoring
Remote Sens. 2019, 11(16), 1940; https://doi.org/10.3390/rs11161940 - 20 Aug 2019
Cited by 4
Abstract
Dense point clouds acquired from Terrestrial Laser Scanners (TLS) have proved to be effective for structural deformation assessment. In the last decade, many researchers have defined methodology and workflow in order to compare different point clouds, with respect to each other or to [...] Read more.
Dense point clouds acquired from Terrestrial Laser Scanners (TLS) have proved to be effective for structural deformation assessment. In the last decade, many researchers have defined methodology and workflow in order to compare different point clouds, with respect to each other or to a known model, assessing the potentialities and limits of this technique. Currently, dense point clouds can be obtained by Close-Range Photogrammetry (CRP) based on a Structure from Motion (SfM) algorithm. This work reports on a comparison between the TLS technique and the Close-Range Photogrammetry using the Structure from Motion algorithm. The analysis of two Reinforced Concrete (RC) beams tested under four-points bending loading is presented. In order to measure displacement distributions, point clouds at different beam loading states were acquired and compared. A description of the instrumentation used and the experimental environment, along with a comprehensive report on the calculations and results obtained is reported. Two kinds of point clouds comparison were investigated: Mesh to mesh and modeling with geometric primitives. The comparison between the mesh to mesh (m2m) approach and the modeling (m) one showed that the latter leads to significantly better results for both TLS and CRP. The results obtained with the TLS for both m2m and m methodologies present a Root Mean Square (RMS) levels below 1 mm, while the CRP method yields to an RMS level of a few millimeters for m2m, and of 1 mm for m. Full article
(This article belongs to the Special Issue Point Cloud Processing and Analysis in Remote Sensing)
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