scholarly journals Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

2014 ◽  
Vol 6 (9) ◽  
pp. 8405-8423 ◽  
Author(s):  
Jixian Zhang ◽  
Minyan Duan ◽  
Qin Yan ◽  
Xiangguo Lin
Keyword(s):  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Analysis Method ◽  
Object Based ◽  
Airborne Lidar Data ◽  
Point Cloud Analysis ◽  
Cloud Analysis

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

scholarly journals Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

Object-Based Point Cloud Analysis for Landslide and Erosion Monitoring

2019 ◽  
Vol 85 (6) ◽  
pp. 455-462 ◽  
Author(s):  
Andreas Mayr ◽  
Martin Rutzinger ◽  
Clemens Geitner
Keyword(s):  
Point Cloud ◽  
Object Based ◽  
Point Cloud Analysis ◽  
Cloud Analysis

scholarly journals Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

2014 ◽  
Vol 6 (11) ◽  
pp. 10733-10749 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Zizheng Wang ◽  
Yihui Lu
Keyword(s):  
Change Detection ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Object Based ◽  
Airborne Lidar Data

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

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