PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification

Airborne laser scanning (ALS) point cloud has been widely used in the fields of ground powerline surveying, forest monitoring, urban modeling, and so on because of the great convenience it brings to people’s daily life. However, the sparsity and uneven distribution of point clouds increases the difficulty of setting uniform parameters for semantic classification. The PointNet++ network is an end-to-end learning network for irregular point data and highly robust to small perturbations of input points along with corruption. It eliminates the need to calculate costly handcrafted features and provides a new paradigm for 3D understanding. However, each local region in the output is abstracted by its centroid and local feature that encodes the centroid’s neighborhood. The feature learned on the centroid point may not contain relevant information of itself for random sampling, especially in large-scale neighborhood balls. Moreover, the centroid point’s global-level information in each sample layer is also not marked. Therefore, this study proposed a modified PointNet++ network architecture which concentrates the point-level and global features on the centroid point towards the local features to facilitate classification. The proposed approach also utilizes a modified Focal Loss function to solve the extremely uneven category distribution on ALS point clouds. An elevation- and distance-based interpolation method is also proposed for the objects in ALS point clouds which exhibit discrepancies in elevation distributions. The experiments on the Vaihingen dataset of the International Society for Photogrammetry and Remote Sensing and the GML(B) 3D dataset demonstrate that the proposed method which provides additional contextual information to support classification achieves high accuracy with simple discriminative models and new state-of-the-art performance in power line categories.

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Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

10.5194/egusphere-egu2020-7378 ◽

2020 ◽

Author(s):

Moritz Bruggisser ◽

Johannes Otepka ◽

Norbert Pfeifer ◽

Markus Hollaus

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Laser Scanner ◽

Point Clouds ◽

Stem Diameter ◽

Forest Monitoring ◽

Individual Tree ◽

Detection Rates ◽

Point Count ◽

Tree Stem

Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation.&#160; The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.

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Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

Remote Sensing ◽

10.3390/rs13112195 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2195

Author(s):

Shiming Li ◽

Xuming Ge ◽

Shengfu Li ◽

Bo Xu ◽

Zhendong Wang

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Registration Method ◽

Cloud Data ◽

Linear Feature ◽

Data Registration ◽

Source Point ◽

Urban Remote Sensing ◽

Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽

10.3390/f12070835 ◽

2021 ◽

Vol 12 (7) ◽

pp. 835

Author(s):

Ville Luoma ◽

Tuomas Yrttimaa ◽

Ville Kankare ◽

Ninni Saarinen ◽

Jiri Pyörälä ◽

...

Keyword(s):

Tree Growth ◽

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Point Clouds ◽

Stem Volume ◽

Accurate Information ◽

Stem Form ◽

Monitoring Period ◽

Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

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Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060367 ◽

2021 ◽

Vol 10 (6) ◽

pp. 367

Author(s):

Simoni Alexiou ◽

Georgios Deligiannakis ◽

Aggelos Pallikarakis ◽

Ioannis Papanikolaou ◽

Emmanouil Psomiadis ◽

...

Keyword(s):

Soil Erosion ◽

Change Detection ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Mean Value ◽

Detection Methods ◽

Mountainous Catchments ◽

Aerial Vehicle ◽

Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

Remote Sensing ◽

10.3390/rs13020261 ◽

2021 ◽

Vol 13 (2) ◽

pp. 261

Author(s):

Francisco Mauro ◽

Andrew T. Hudak ◽

Patrick A. Fekety ◽

Bryce Frank ◽

Hailemariam Temesgen ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Field Measurements ◽

Point Clouds ◽

Semiparametric Models ◽

Airborne Laser Scanning ◽

Parametric Models ◽

Fuel Load ◽

Forest Fuels ◽

Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

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Application of close range photogrammetry in structural health monitoring by processing generated point cloud datasets

10.2749/ghent.2021.0450 ◽

2021 ◽

Author(s):

Ali Mirzazade ◽

Cosmin Popescu ◽

Thomas Blanksvärd ◽

Björn Täljsten

Keyword(s):

Health Monitoring ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Optical Data ◽

Small Scale ◽

Relevant Parameter ◽

Bridge Inspection ◽

Close Range Photogrammetry ◽

Close Range

In bridge inspection, vertical displacement is a relevant parameter for both short and long-term health monitoring. Assessing change in deflections could also simplify the assessment work for inspectors. Recent developments in digital camera technology and photogrammetry software enables point cloud with colour information (RGB values) to be generated. Thus, close range photogrammetry offers the potential of monitoring big and small-scale damages by point clouds. The current paper aims to monitor geometrical deviations in Pahtajokk Bridge, Northern Sweden, using an optical data acquisition technique. The bridge in this study is scanned two times by almost one year a part. After point cloud generation the datasets were compared to detect geometrical deviations. First scanning was carried out by both close range photogrammetry (CRP) and terrestrial laser scanning (TLS), while second scanning was performed by CRP only. Analyzing the results has shown the potential of CRP in bridge inspection.

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VALIDATION OF A UAV-DERIVED POINT CLOUD BY SEMANTIC CLASSIFICATION AND COMPARISON WITH TLS DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-83-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 83-90

Author(s):

M. Franzini ◽

V. Casella ◽

P. Marchese ◽

M. Marini ◽

G. Della Porta ◽

...

Keyword(s):

Point Cloud ◽

Laser Scanner ◽

Point Clouds ◽

Aerial Survey ◽

Gradual Transition ◽

Support Vector ◽

Uniform Density ◽

Mutual Distance ◽

Semantic Classification ◽

Cost Efficient

Abstract. Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.

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ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-343-2018 ◽

2018 ◽

Vol XLII-4 ◽

pp. 343-349 ◽

Cited By ~ 1

Author(s):

M. Lemmens

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Decision Rules ◽

Point Clouds ◽

Bench Mark ◽

Automated Classification ◽

Initial Experiment ◽

Data Set ◽

Knowledge Based

Abstract. A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79% for the Paris-rue-Madame point cloud bench mark data set.

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EXPLORING ALS AND DIM DATA FOR SEMANTIC SEGMENTATION USING CNNS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-347-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 347-354 ◽

Cited By ~ 5

Author(s):

F. Politz ◽

M. Sester

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Semantic Segmentation ◽

Point Clouds ◽

Good Alternative ◽

Aerial Images ◽

Learning Approaches ◽

Advantages And Disadvantages ◽

Sensing Applications ◽

High Level

Abstract. Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96% in an ALS and 83% in a DIM test set.

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ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-797-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 797-804 ◽

Cited By ~ 4

Author(s):

W. Ostrowski ◽

M. Pilarska ◽

J. Charyton ◽

K. Bakuła

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Large Scale ◽

Point Clouds ◽

3D Models ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Statistical Parameters ◽

Airborne Laser ◽

Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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