Single Part of Building Extraction from Dense Matching Point Cloud

2018 ◽  
Vol 45 (7) ◽  
pp. 0710004
Author(s):  
闫利 Yan Li ◽  
魏峰 Wei Feng
Keyword(s):  
Point Cloud ◽  
Building Extraction ◽  
Dense Matching ◽  
Single Part

scholarly journals An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1680
Author(s):  
Chenguang Dai ◽  
Zhenchao Zhang ◽  
Dong Lin
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Recall Rate ◽  
Data Sets ◽  
Building Extraction ◽  
Dense Matching ◽  
Object Based ◽  
Alignment Errors ◽  
Precision Rate

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

scholarly journals UAV-BASED POINT CLOUD GENERATION FOR OPEN-PIT MINE MODELLING

2015 ◽  
Vol XL-1/W4 ◽  
pp. 313-320 ◽  
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
System Integration ◽  
Point Cloud ◽  
Three Dimensional ◽  
3D Modelling ◽  
Bundle Adjustment ◽  
Aerial Imagery ◽  
Open Pit ◽  
Sensor Calibration ◽  
Dense Matching ◽  
Mapping Techniques

Along with the advancement of unmanned aerial vehicles (UAVs), improvement of high-resolution cameras and development of vision-based mapping techniques, unmanned aerial imagery has become a matter of remarkable interest among researchers and industries. These images have the potential to provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modelling. In this paper, we present our theoretical and technical experiments regarding the development, implementation and evaluation of a UAV-based photogrammetric system for precise 3D modelling. This system was preliminarily evaluated for the application of gravel-pit surveying. The hardware of the system includes an electric powered helicopter, a 16-megapixels visible camera and inertial navigation system. The software of the system consists of the in-house programs built for sensor calibration, platform calibration, system integration and flight planning. It also includes the algorithms developed for structure from motion (SfM) computation including sparse matching, motion estimation, bundle adjustment and dense matching.

scholarly journals CHANGING THE PRODUCTION PIPELINE – USE OF OBLIQUE AERIAL CAMERAS FOR MAPPING PURPOSES

2016 ◽  
Vol XLI-B4 ◽  
pp. 631-637
Author(s):  
K. Moe ◽  
I. Toschi ◽  
D. Poli ◽  
F. Lago ◽  
C. Schreiner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Reference Data ◽  
Ground Truth ◽  
Building Extraction ◽  
Camera System ◽  
Ground Truth Data ◽  
Production Pipeline ◽  
Dense Image ◽  
Oblique Images

This paper discusses the potential of current photogrammetric multi-head oblique cameras, such as UltraCam Osprey, to improve the efficiency of standard photogrammetric methods for surveying applications like inventory surveys and topographic mapping for public administrations or private customers. <br><br> In 2015, Terra Messflug (TM), a subsidiary of Vermessung AVT ZT GmbH (Imst, Austria), has flown a number of urban areas in Austria, Czech Republic and Hungary with an UltraCam Osprey Prime multi-head camera system from Vexcel Imaging. In collaboration with FBK Trento (Italy), the data acquired at Imst (a small town in Tyrol, Austria) were analysed and processed to extract precise 3D topographic information. The Imst block comprises 780 images and covers an area of approx. 4.5 km by 1.5 km. Ground truth data is provided in the form of 6 GCPs and several check points surveyed with RTK GNSS. Besides, 3D building data obtained by photogrammetric stereo plotting from a 5 cm nadir flight and a LiDAR point cloud with 10 to 20 measurements per m² are available as reference data or for comparison. The photogrammetric workflow, from flight planning to Dense Image Matching (DIM) and 3D building extraction, is described together with the achieved accuracy. For each step, the differences and innovation with respect to standard photogrammetric procedures based on nadir images are shown, including high overlaps, improved vertical accuracy, and visibility of areas masked in the standard vertical views. Finally the advantages of using oblique images for inventory surveys are demonstrated.

scholarly journals REGION-BASED 3D SURFACE RECONSTRUCTION USING IMAGES ACQUIRED BY LOW-COST UNMANNED AERIAL SYSTEMS

2015 ◽  
Vol XL-1/W4 ◽  
pp. 167-173 ◽  
Author(s):  
Z. Lari ◽  
A. Al-Rawabdeh ◽  
F. He ◽  
A. Habib ◽  
N. El-Sheimy
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Low Cost ◽  
Geospatial Data ◽  
Unmanned Aerial Systems ◽  
3D Surface Reconstruction ◽  
Planar Surfaces ◽  
3D Surface ◽  
Dense Matching ◽  
Aerial Systems

Accurate 3D surface reconstruction of our environment has become essential for an unlimited number of emerging applications. In the past few years, Unmanned Aerial Systems (UAS) are evolving as low-cost and flexible platforms for geospatial data collection that could meet the needs of aforementioned application and overcome limitations of traditional airborne and terrestrial mobile mapping systems. Due to their payload restrictions, these systems usually include consumer-grade imaging and positioning sensor which will negatively impact the quality of the collected geospatial data and reconstructed surfaces. Therefore, new surface reconstruction surfaces are needed to mitigate the impact of using low-cost sensors on the final products. To date, different approaches have been proposed to for 3D surface construction using overlapping images collected by imaging sensor mounted on moving platforms. In these approaches, 3D surfaces are mainly reconstructed based on dense matching techniques. However, generated 3D point clouds might not accurately represent the scanned surfaces due to point density variations and edge preservation problems. In order to resolve these problems, a new region-based 3D surface renostruction trchnique is introduced in this paper. This approach aims to generate a 3D photo-realistic model of individually scanned surfaces within the captured images. This approach is initiated by a Semi-Global dense Matching procedure is carried out to generate a 3D point cloud from the scanned area within the collected images. The generated point cloud is then segmented to extract individual planar surfaces. Finally, a novel region-based texturing technique is implemented for photorealistic reconstruction of the extracted planar surfaces. Experimental results using images collected by a camera mounted on a low-cost UAS demonstrate the feasibility of the proposed approach for photorealistic 3D surface reconstruction.

scholarly journals A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

2019 ◽  
Vol 11 (14) ◽  
pp. 1636 ◽  
Author(s):  
Xudong Lai ◽  
Jingru Yang ◽  
Yongxu Li ◽  
Mingwei Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Land Management ◽  
Extraction Method ◽  
Point Cloud ◽  
Texture Feature ◽  
Texture Features ◽  
Extraction Methods ◽  
Building Extraction ◽  
Texture Feature Extraction

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

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