scholarly journals Rule-based Classification of Airborne Laser Scanner Data for Automatic Extraction of 3D Objects in the Urban Area

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ngoc Quy BUI ◽  
Dinh Hien LE ◽  
Anh Quan DUONG ◽  
Quoc Long NGUYEN
Keyword(s):  
Land Use ◽  
Point Cloud ◽  
Laser Scanner ◽  
Building Detection ◽  
Land Use Classification ◽  
Close Point ◽  
Airborne Laser Scanner ◽  
Ndvi Index ◽  
Point Data ◽  
Point Cloud Classification

LiDAR technology has been widely adopted as a proper method for land cover classification.Recently with the development of technology, LiDAR systems can now capture high-resolutionmultispectral bands images with high-density LiDAR point cloud simultaneously. Therefore, it opens newopportunities for more precise automatic land-use classification methods by utilizing LiDAR data. Thisarticle introduces a combining technique of point cloud classification algorithms. The algorithms includeground detection, building detection, and close point classification - the classification is based on pointclouds’ attributes. The main attributes are heigh, intensity, and NDVI index calculated from 4 bands ofcolors extracted from multispectral images for each point. Data of the Leica City Mapper LiDAR systemin an area of 80 ha in Quang Xuong town, Thanh Hoa province, Vietnam was used to deploy theclassification. The data is classified into eight different types of land use consist of asphalt road, otherground, low vegetation, medium vegetation, high vegetation, building, water, and other objects. Theclassification workflow was implemented in the TerraSolid suite, with the result of the automation processcame out with 97% overall accuracy of classification points. The

Detection of Building in Airborne Laser Scanner Data and Aerial Images

2012 ◽  
Vol 11 ◽  
pp. 7-13
Author(s):  
Dilli Raj Bhandari
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Aerial Images ◽  
Spectral Information ◽  
Surface Model ◽  
World Population ◽  
Data Sets ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser

The automatic extraction of the objects from airborne laser scanner data and aerial images has been a topic of research for decades. Airborne laser scanner data are very efficient source for the detection of the buildings. Half of the world population lives in urban/suburban areas, so detailed, accurate and up-to-date building information is of great importance to every resident, government agencies, and private companies. The main objective of this paper is to extract the features for the detection of building using airborne laser scanner data and aerial images. To achieve this objective, a method of integration both LiDAR and aerial images has been explored: thus the advantages of both data sets are utilized to derive the buildings with high accuracy. Airborne laser scanner data contains accurate elevation information in high resolution which is very important feature to detect the elevated objects like buildings and the aerial image has spectral information and this spectral information is an appropriate feature to separate buildings from the trees. Planner region growing segmentation of LiDAR point cloud has been performed and normalized digital surface model (nDSM) is obtained by subtracting DTM from the DSM. Integration of the nDSM, aerial images and the segmented polygon features from the LiDAR point cloud has been carried out. The optimal features for the building detection have been extracted from the integration result. Mean height value of the nDSM, Normalized difference vegetation index (NDVI) and the standard deviation of the nDSM are the effective features. The accuracy assessment of the classification results obtained using the calculated attributes was done. Assessment result yielded an accuracy of almost 92 % explaining the features which are extracted by integrating the two data sets was large extent, effective for the automatic detection of the buildings.

scholarly journals PRECISE INDOOR LOCALIZATION FOR MOBILE LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 1-6 ◽  
Author(s):  
R. Kaijaluoto ◽  
A. Hyyppä
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Indoor Localization ◽  
Collection Efficiency ◽  
Laser Scanner ◽  
Functional Method ◽  
Single View ◽  
Laser Scanners ◽  
Point Data ◽  
High Level

Accurate 3D data is of high importance for indoor modeling for various applications in construction, engineering and cultural heritage documentation. For the lack of GNSS signals hampers use of kinematic platforms indoors, TLS is currently the most accurate and precise method for collecting such a data. Due to its static single view point data collection, excessive time and data redundancy are needed for integrity and coverage of data. However, localization methods with affordable scanners are used for solving mobile platform pose problem. The aim of this study was to investigate what level of trajectory accuracies can be achieved with high quality sensors and freely available state of the art planar SLAM algorithms, and how well this trajectory translates to a point cloud collected with a secondary scanner. <br><br> In this study high precision laser scanners were used with a novel way to combine the strengths of two SLAM algorithms into functional method for precise localization. We collected five datasets using Slammer platform with two laser scanners, and processed them with altogether 20 different parameter sets. The results were validated against TLS reference. The results show increasing scan frequency improves the trajectory, reaching 20 mm RMSE levels for the best performing parameter sets. Further analysis of the 3D point cloud showed good agreement with TLS reference with 17 mm positional RMSE. With precision scanners the obtained point cloud allows for high level of detail data for indoor modeling with accuracies close to TLS at best with vastly improved data collection efficiency.

scholarly journals Towards Efficient Implementation of an Octree for a Large 3D Point Cloud

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4398 ◽  
Author(s):  
Soohee Han
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Main Memory ◽  
3D Point Cloud ◽  
Airborne Laser Scanner ◽  
Memory Efficiency ◽  
3D Point Clouds ◽  
Short Tunnel ◽  
Better Than

The present study introduces an efficient algorithm to construct a file-based octree for a large 3D point cloud. However, the algorithm was very slow compared with a memory-based approach, and got even worse when using a 3D point cloud scanned in longish objects like tunnels and corridors. The defects were addressed by implementing a semi-isometric octree group. The approach implements several semi-isometric octrees in a group, which tightly covers the 3D point cloud, though each octree along with its leaf node still maintains an isometric shape. The proposed approach was tested using three 3D point clouds captured in a long tunnel and a short tunnel by a terrestrial laser scanner, and in an urban area by an airborne laser scanner. The experimental results showed that the performance of the semi-isometric approach was not worse than a memory-based approach, and quite a lot better than a file-based one. Thus, it was proven that the proposed semi-isometric approach achieves a good balance between query performance and memory efficiency. In conclusion, if given enough main memory and using a moderately sized 3D point cloud, a memory-based approach is preferable. When the 3D point cloud is larger than the main memory, a file-based approach seems to be the inevitable choice, however, the semi-isometric approach is the better option.

scholarly journals REGISTRATION OF UAV DATA AND ALS DATA USING POINT TO DEM DISTANCES FOR BATHYMETRIC CHANGE DETECTION

2018 ◽  
Vol XLII-1 ◽  
pp. 51-58 ◽  
Author(s):  
R. Boerner ◽  
Y. Xu ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Rural Areas ◽  
Point Cloud ◽  
Laser Scanner ◽  
Iteration Process ◽  
Point Clouds ◽  
Iteration Step ◽  
Target Point ◽  
General Technique ◽  
Airborne Laser Scanner ◽  
Planar Structures

<p><strong>Abstract.</strong> This paper shows a method to register point clouds from images of UAV-mounted airborne cameras as well as airborne laser scanner data. The focus is a general technique which does rely neither on linear or planar structures nor on the point cloud density. Therefore, the proposed approach is also suitable for rural areas and water bodies captured via different sensor configurations. This approach is based on a regular 2.5D grid generated from the segmented ground points of the 3D point cloud. It is assumed that initial values for the registration are already estimated, e.g. by measured exterior orientation parameters with the UAV mounted GNSS and IMU. These initial parameters are finely tuned by minimizing the distances between the 3D points of a target point cloud to the generated grid of the source point cloud in an iteration process. To eliminate outliers (e.g., vegetation points) a threshold for the distances is defined dynamically at each iteration step, which filters ground points during the registration. The achieved accuracy of the registration is up to 0.4<span class="thinspace"></span>m in translation and up to 0.3<span class="thinspace"></span>degrees in rotation, by using a raster size of the DEM of 2<span class="thinspace"></span>m. Considering the ground sampling distance of the airborne data which is up to 0.4<span class="thinspace"></span>m between the scan lines, this result is comparable to the result achieved by an ICP algorithm, but the proposed approach does not rely on point densities and is therefore able to solve registrations where the ICP have difficulties.</p>

scholarly journals AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 419-423 ◽  
Author(s):  
Yandong Wang
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Automatic Generation ◽  
Aerial Imagery ◽  
Building Detection ◽  
New Approach ◽  
Building Roof ◽  
High Resolution Imagery ◽  
Point Cloud Classification

In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

scholarly journals AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 419-423 ◽  
Author(s):  
Yandong Wang
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Automatic Generation ◽  
Aerial Imagery ◽  
Building Detection ◽  
New Approach ◽  
Building Roof ◽  
High Resolution Imagery ◽  
Point Cloud Classification

In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

scholarly journals BLOCK ADJUSTMENT AND IMAGE MATCHING OF WORLDVIEW-3 STEREO PAIRS AND ACCURACY EVALUATION

2018 ◽  
Vol XLII-3 ◽  
pp. 2473-2477
Author(s):  
C. Zuo ◽  
X. Xiao ◽  
Q. Hou ◽  
B. Li
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Large Scale ◽  
Laser Scanner ◽  
Stereo Image ◽  
Topographic Mapping ◽  
Accuracy Evaluation ◽  
Airborne Laser Scanner ◽  
Earth Observation Satellite ◽  
Stereo Image Matching

WorldView-3, as a high-resolution commercial earth observation satellite, which is launched by Digital Global, provides panchromatic imagery of 0.31&amp;thinsp;m resolution. The positioning accuracy is less than 3.5 meter CE90 without ground control, which can use for large scale topographic mapping. This paper presented the block adjustment for WorldView-3 based on RPC model and achieved the accuracy of 1&amp;thinsp;:&amp;thinsp;2000 scale topographic mapping with few control points. On the base of stereo orientation result, this paper applied two kinds of image matching algorithm for DSM extraction: LQM and SGM. Finally, this paper compared the accuracy of the point cloud generated by the two image matching methods with the reference data which was acquired by an airborne laser scanner.<br> The results showed that the RPC adjustment model of WorldView-3 image with small number of GCPs could satisfy the requirement of Chinese Surveying and Mapping regulations for 1&amp;thinsp;:&amp;thinsp;2000 scale topographic maps. And the point cloud result obtained through WorldView-3 stereo image matching had higher elevation accuracy, the RMS error of elevation for bare ground area is 0.45&amp;thinsp;m, while for buildings the accuracy can almost reach 1 meter.

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