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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 47245

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Dr. Sander Oude Elberink

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Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: point cloud processing; object detection and classification of MLS and ALS point clouds; 3D modelling of buildings; detection and modelling of infrastructural objects; fusing point clouds with large-scale topographic map data
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Dr. Kourosh Khoshelham

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Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: photogrammetry; 3D computer vision; remote sensing; machine learning; deep learning; automated interpretation of imagery and point clouds
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Prof. Dr. Cheng Wang

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Department of Computer Science, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, Fujian, China
Interests: 3D vision; LiDAR; mobile mapping; geospatial big data analysis
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Special Issue Information

Dear Colleagues,

Accurate 3D digital representations of the natural and built environments play an important role in a wide range of applications. Laser scanning is the principal technology for efficient 3D data capture in the form of point clouds. Point clouds can be generated from laser scanners or derived from image matching techniques, although the focus in this Special Issue is on laser scanner point clouds. However, a point is just a point. It is the context that delivers the information on the object behind the point. Research challenges in the field of laser scanning and point cloud processing range from calibration, fusion, interpretation, and modelling, to efficient information extraction and visualization topics. The scope of this Special Issue is therefor rather broad in the sense that we would like to include indoor, mobile, and airborne laser scanners, in combination with point cloud processing algorithms, for a broad range of applications.

Dr. Sander Oude Elberink
Dr. Kourosh Khoshelham
Prof. Cheng Wang

Guest Editor

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Keywords

Point cloud
Laser scanning
Classification
Segmentation
Calibration
3D modelling
Pose estimation Change detection
Deep learning

Published Papers (11 papers)

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Research

23 pages, 11245 KiB

Open AccessArticle

Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

by Chi-Kuei Wang and Nadeem Fareed

Remote Sens. 2021, 13(3), 463; https://doi.org/10.3390/rs13030463 - 28 Jan 2021

Cited by 8 | Viewed by 4155

Abstract

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km², including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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24 pages, 6942 KiB

Open AccessArticle

Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks

by Dilong Li, Xin Shen, Yongtao Yu, Haiyan Guan, Jonathan Li, Guo Zhang and Deren Li

Remote Sens. 2020, 12(19), 3186; https://doi.org/10.3390/rs12193186 - 29 Sep 2020

Cited by 28 | Viewed by 3741

Abstract

Building extraction has attracted much attentions for decades as a prerequisite for many applications and is still a challenging topic in the field of photogrammetry and remote sensing. Due to the lack of spectral information, massive data processing, and approach universality, building extraction from point clouds is still a thorny and challenging problem. In this paper, a novel deep-learning-based framework is proposed for building extraction from point cloud data. Specifically, first, a sample generation method is proposed to split the raw preprocessed multi-spectral light detection and ranging (LiDAR) data into numerous samples, which are directly fed into convolutional neural networks and completely cover the original inputs. Then, a graph geometric moments (GGM) convolution is proposed to encode the local geometric structure of point sets. In addition, a hierarchical architecture equipped with GGM convolution, called GGM convolutional neural networks, is proposed to train and recognize building points. Finally, the test scenes with varying sizes can be fed into the framework and obtain a point-wise extraction result. We evaluate the proposed framework and methods on the airborne multi-spectral LiDAR point clouds collected by an Optech Titan system. Compared with previous state-of-the-art networks, which are designed for point cloud segmentation, our method achieves the best performance with a correctness of 95.1%, a completeness of 93.7%, an F-measure of 94.4%, and an intersection over union (IoU) of 89.5% on two test areas. The experimental results confirm the effectiveness and efficiency of the proposed framework and methods. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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22 pages, 12946 KiB

Open AccessArticle

A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

by Tengfei Zhou, Xiaojun Cheng, Peng Lin, Zhenlun Wu and Ensheng Liu

Remote Sens. 2020, 12(18), 2923; https://doi.org/10.3390/rs12182923 - 09 Sep 2020

Cited by 6 | Viewed by 2410

Abstract

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10⁻⁴ to 10⁻⁸ (a priori known) and 10⁻⁷ (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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24 pages, 8434 KiB

Open AccessArticle

Classification of Point Clouds for Indoor Components Using Few Labeled Samples

by Hangbin Wu, Huimin Yang, Shengyu Huang, Doudou Zeng, Chun Liu, Hao Zhang, Chi Guo and Long Chen

Remote Sens. 2020, 12(14), 2181; https://doi.org/10.3390/rs12142181 - 08 Jul 2020

Cited by 3 | Viewed by 3064

Abstract

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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24 pages, 4852 KiB

Open AccessArticle

A Flexible Inference Machine for Global Alignment of Wall Openings

by Jiaqiang Li, Biao Xiong, Rongjun Qin and Armin Gruen

Remote Sens. 2020, 12(12), 1968; https://doi.org/10.3390/rs12121968 - 19 Jun 2020

Cited by 4 | Viewed by 2244

Abstract

Openings such as windows and doors are essential components of architectural wall surfaces. It is still a challenge to reconstruct them robustly from unstructured 3D point clouds because of occlusions, noises and non-uniformly distributed points. Current research primarily focuses on meliorating the robustness of detection and pays little attention to the geometric correctness. To improve the reconstruction quality, assumptions on the opening layout are usually applied as rules to support the reconstruction algorithm. The commonly used assumptions, such as the strict grid and symmetry pattern, however, are not suitable in many cases. In this paper, we propose a novel approach, named an inference machine, to identify and use flexible rules in wall opening modelling. Our method first detects and models openings through a data-driven method and then refines the opening boundaries by global and flexible rules. The key is to identify the global flexible rules from the detected openings, composed by various combinations of alignments. As our method is oblivious of the type of architectural layout, it can be applied to both interior wall surfaces and exterior building facades. We demonstrate the flexibility of our approach in both outdoor and indoor scenes with a variety of opening layouts. The qualitative and quantitative evaluation results indicate the potential of the approach to be a general method in opening detection and modelling. However, this data-driven method suffers from the existence of occlusions and non-planar wall surfaces. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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18 pages, 28481 KiB

Open AccessArticle

TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas

by Jingwei Zhu, Joachim Gehrung, Rong Huang, Björn Borgmann, Zhenghao Sun, Ludwig Hoegner, Marcus Hebel, Yusheng Xu and Uwe Stilla

Remote Sens. 2020, 12(11), 1875; https://doi.org/10.3390/rs12111875 - 09 Jun 2020

Cited by 40 | Viewed by 5319

Abstract

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360

°

laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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19 pages, 4498 KiB

Open AccessArticle

Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences

by Nadisson Luis Pavan, Daniel Rodrigues dos Santos and Kourosh Khoshelham

Remote Sens. 2020, 12(7), 1127; https://doi.org/10.3390/rs12071127 - 02 Apr 2020

Cited by 17 | Viewed by 3574

Abstract

Registration of point clouds is a central problem in many mapping and monitoring applications, such as outdoor and indoor mapping, high-speed railway track inspection, heritage documentation, building information modeling, and others. However, ensuring the global consistency of the registration is still a challenging task when there are multiple point clouds because the different scans should be transformed into a common coordinate frame. The aim of this paper is the registration of multiple terrestrial laser scanner point clouds. We present a plane-based matching algorithm to find plane-to-plane correspondences using a new parametrization based on complex numbers. The multiplication of complex numbers is based on analysis of the quadrants to avoid the ambiguity in the calculation of the rotation angle formed between normal vectors of adjacent planes. As a matching step may contain several matrix operations, our strategy is applied to reduce the number of mathematical operations. We also design a novel method for global refinement of terrestrial laser scanner data based on plane-to-plane correspondences. The rotation parameters are globally refined using operations of quaternion multiplication, while the translation parameters are refined using the parameters of planes. The global refinement is done non-iteratively. The experimental results show that the proposed plane-based matching algorithm efficiently finds plane correspondences in partial overlapping scans providing approximate values for the global registration, and indicate that an accuracy better than 8 cm can be achieved by using our global fine plane-to-plane registration method. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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18 pages, 14778 KiB

Open AccessArticle

Using Training Samples Retrieved from a Topographic Map and Unsupervised Segmentation for the Classification of Airborne Laser Scanning Data

by Zhishuang Yang, Wanshou Jiang, Yaping Lin and Sander Oude Elberink

Remote Sens. 2020, 12(5), 877; https://doi.org/10.3390/rs12050877 - 09 Mar 2020

Cited by 7 | Viewed by 3405

Abstract

The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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26 pages, 8582 KiB

Open AccessArticle

Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo

by Ha Tran and Kourosh Khoshelham

Remote Sens. 2020, 12(5), 838; https://doi.org/10.3390/rs12050838 - 05 Mar 2020

Cited by 36 | Viewed by 4604

Abstract

Automated reconstruction of Building Information Models (BIMs) from point clouds has been an intensive and challenging research topic for decades. Traditionally, 3D models of indoor environments are reconstructed purely by data-driven methods, which are susceptible to erroneous and incomplete data. Procedural-based methods such as the shape grammar are more robust to uncertainty and incompleteness of the data as they exploit the regularity and repetition of structural elements and architectural design principles in the reconstruction. Nevertheless, these methods are often limited to simple architectural styles: the so-called Manhattan design. In this paper, we propose a new method based on a combination of a shape grammar and a data-driven process for procedural modelling of indoor environments from a point cloud. The core idea behind the integration is to apply a stochastic process based on reversible jump Markov Chain Monte Carlo (rjMCMC) to guide the automated application of grammar rules in the derivation of a 3D indoor model. Experiments on synthetic and real data sets show the applicability of the method to efficiently generate 3D indoor models of both Manhattan and non-Manhattan environments with high accuracy, completeness, and correctness. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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19 pages, 15125 KiB

Open AccessArticle

Tunnel Monitoring and Measuring System Using Mobile Laser Scanning: Design and Deployment

by Haili Sun, Zhengwen Xu, Lianbi Yao, Ruofei Zhong, Liming Du and Hangbin Wu

Remote Sens. 2020, 12(4), 730; https://doi.org/10.3390/rs12040730 - 22 Feb 2020

Cited by 44 | Viewed by 8975

Abstract

The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human–computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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24 pages, 16452 KiB

Open AccessArticle

Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds

by Yanyang Xiao, Zhonggui Chen, Zhengtao Lin, Juan Cao, Yongjie Jessica Zhang, Yangbin Lin and Cheng Wang

Remote Sens. 2020, 12(3), 473; https://doi.org/10.3390/rs12030473 - 02 Feb 2020

Cited by 8 | Viewed by 3891

Abstract

Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing)

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