Verfahren zur Registrierung von Laserscannerdaten in Waldbeständen | Methods for registration laser scanner point clouds in forest stands

2011 ◽  
Vol 162 (6) ◽  
pp. 178-185 ◽  
Author(s):  
Anne Bienert ◽  
Katharina Pech ◽  
Hans-Gerd Maas
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Forest Stand ◽  
Geometric Transformation ◽  
Complex Object ◽  
Tree Detection ◽  
Airborne Laser ◽  
Coarse Registration ◽  
Fine Registration

Laser scanning is a fast and efficient 3-D measurement technique to capture surface points describing the geometry of a complex object in an accurate and reliable way. Besides airborne laser scanning, terrestrial laser scanning finds growing interest for forestry applications. These two different recording platforms show large differences in resolution, recording area and scan viewing direction. Using both datasets for a combined point cloud analysis may yield advantages because of their largely complementary information. In this paper, methods will be presented to automatically register airborne and terrestrial laser scanner point clouds of a forest stand. In a first step, tree detection is performed in both datasets in an automatic manner. In a second step, corresponding tree positions are determined using RANSAC. Finally, the geometric transformation is performed, divided in a coarse and fine registration. After a coarse registration, the fine registration is done in an iterative manner (ICP) using the point clouds itself. The methods are tested and validated with a dataset of a forest stand. The presented registration results provide accuracies which fulfill the forestry requirements.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103 ◽  
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

scholarly journals EVALUATING THE POSSIBILITY OF TREE SPECIES CLASSIFICATION WITH DUAL-WAVELENGTH ALS DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1097-1103
Author(s):  
M. Pilarska ◽  
W. Ostrowski
Keyword(s):  
Spatial Data ◽  
Tree Species ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Tree Canopy ◽  
Species Classification ◽  
Airborne Laser ◽  
Simultaneous Acquisition

<p><strong>Abstract.</strong> Airborne laser scanning (ALS) plays an important role in spatial data acquisition. One of the advantages of this technique is laser beam penetration through vegetation, which makes it possible to not only obtain data on the tree canopy but also within and under the canopy. In recent years, multi-wavelength airborne laser scanning has been developed. This technique consists of simultaneous acquisition of point clouds in more than one band. The aim of this experiment was to examine and assess the possibilities of tree segmentation and species classification in an urban area. In this experiment, point clouds registered in two wavelengths (532 and 1064&amp;thinsp;nm) were used for tree segmentation and species classification. The data were acquired with a Riegl VQ-1560i-DW laser scanner over Elblag, Poland, during August 2018. Tree species collected by a botanist team within terrain measurements were used as a reference in the classification process. Within the experiment segmentation and classification process were performed. Regarding the segmentation, TerraScan software and Li et al.’s algorithm, implemented in LidR package were used. Results from both methods are clearly over-segmented in comparison to the manual segments. In Terrasolid segmentation, single reference segments are over-segmented in 28% of cases, whereas, for LidR, over-segmentation occurred in 73% of the segments. According the classification results, Thuja, Salix and Betula were the species, for which the highest classification accuracy was achieved.</p>

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 461-467 ◽  
Author(s):  
T. Zieher ◽  
M. Bremer ◽  
M. Rutzinger ◽  
J. Pfeiffer ◽  
P. Fritzmann ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Additional Information ◽  
3D Point Clouds ◽  
Airborne Laser ◽  
Displacement Vectors ◽  
Multi Temporal ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4&amp;thinsp;cm within 2.2 years, 2.9&amp;thinsp;cm a<sup>&amp;minus;1</sup>; laser scanning data: 13.2&amp;thinsp;cm within 5.4 years, 2.4&amp;thinsp;cm a<sup>&amp;minus;1</sup>) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.</p>

scholarly journals COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS

2016 ◽  
Vol III-5 ◽  
pp. 97-103
Author(s):  
Jinhu Wang ◽  
Roderik Lindenbergh ◽  
Yueqian Shen ◽  
Massimo Menenti
Keyword(s):  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Data Sampling ◽  
Surface Geometry ◽  
Registration Method ◽  
Input Point ◽  
Coarse Registration ◽  
Point Distance ◽  
Fine Registration

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6&thinsp;mm and 9.5&thinsp;mm respectively, which are adequate for fine registration.

scholarly journals Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2431
Author(s):  
Yongjian Fu ◽  
Zongchun Li ◽  
Wenqi Wang ◽  
Hua He ◽  
Feng Xiong ◽  
...  
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Target Point ◽  
Registration Method ◽  
Geometric Consistency ◽  
Coarse Registration ◽  
Eigenvalue Statistic ◽  
Pairwise Registration ◽  
Coarse To Fine ◽  
Fine Registration

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

scholarly journals Estimation of Vegetation-Induced Flow Resistance for Hydraulic Computations Using Airborne Laser Scanning Data

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1864
Author(s):  
Peter Mewis
Keyword(s):  
Laser Scanning ◽  
Flow Resistance ◽  
Laser Scanner ◽  
Airborne Laser Scanning ◽  
Density Parameter ◽  
Vegetation Density ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Bed Roughness ◽  
Available Information

The effect of vegetation in hydraulic computations can be significant. This effect is important for flood computations. Today, the necessary terrain information for flood computations is obtained by airborne laser scanning techniques. The quality and density of the airborne laser scanning information allows for more extensive use of these data in flow computations. In this paper, known methods are improved and combined into a new simple and objective procedure to estimate the hydraulic resistance of vegetation on the flow in the field. State-of-the-art airborne laser scanner information is explored to estimate the vegetation density. The laser scanning information provides the base for the calculation of the vegetation density parameter ωp using the Beer–Lambert law. In a second step, the vegetation density is employed in a flow model to appropriately account for vegetation resistance. The use of this vegetation parameter is superior to the common method of accounting for the vegetation resistance in the bed resistance parameter for bed roughness. The proposed procedure utilizes newly available information and is demonstrated in an example. The obtained values fit very well with the values obtained in the literature. Moreover, the obtained information is very detailed. In the results, the effect of vegetation is estimated objectively without the assignment of typical values. Moreover, a more structured flow field is computed with the flood around denser vegetation, such as groups of bushes. A further thorough study based on observed flow resistance is needed.

scholarly journals An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2938
Author(s):  
Feng Li ◽  
Haihong Zhu ◽  
Zhenwei Luo ◽  
Hang Shen ◽  
Lin Li
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Superior Performance ◽  
Cloth Simulation ◽  
Cloud Data ◽  
Interpolation Filter ◽  
Landscape Characteristics ◽  
Surface Interpolation ◽  
Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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