Exploring Model Transfer Potential for Airborne LiDAR Point Cloud Classification

2019 ◽  
pp. 39-51
Author(s):  
Yuzhun Lin ◽  
Chuan Zhao ◽  
Daoji Li ◽  
Junfeng Xu ◽  
Baoming Zhang
Keyword(s):  
Point Cloud ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Model Transfer ◽  
Point Cloud Classification ◽  
Transfer Potential

Small Sample Airborne LiDAR Point Cloud Classification Based on Transfer Learning

2020 ◽  
Vol 47 (11) ◽  
pp. 1110002
Author(s):  
雷相达 Lei Xiangda ◽  
王宏涛 Wang Hongtao ◽  
赵宗泽 Zhao Zongze
Keyword(s):  
Transfer Learning ◽  
Point Cloud ◽  
Small Sample ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Point Cloud Classification

Airborne LiDAR point cloud classification using transfer learning

2019 ◽  
Vol 27 (7) ◽  
pp. 1601-1612
Author(s):  
赵 传 ZHAO Chuan ◽  
张保明 ZHANG Bao-ming ◽  
余东行 YU Dong-hang ◽  
郭海涛 GUO Hai-tao ◽  
卢 俊 LU Jun
Keyword(s):  
Transfer Learning ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Point Cloud Classification

A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification

2016 ◽  
Vol 53 (8) ◽  
pp. 082802 ◽  
Author(s):  
张爱武 Zhang Aiwu ◽  
肖涛 Xiao Tao ◽  
段乙好 Duan Yihao
Keyword(s):  
Feature Selection ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Adaptive Feature Selection ◽  
Selection For ◽  
Point Cloud Classification

A novel semi-supervised method for airborne LiDAR point cloud classification

2021 ◽  
Vol 180 ◽  
pp. 117-129
Author(s):  
Xiang Li ◽  
Congcong Wen ◽  
Qiming Cao ◽  
Yanlei Du ◽  
Yi Fang
Keyword(s):  
Point Cloud ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals MAPPING URBAN TREE CANOPY COVER USING FUSED AIRBORNE LIDAR AND SATELLITE IMAGERY DATA

2016 ◽  
Vol III-7 ◽  
pp. 181-186
Author(s):  
Ebadat G. Parmehr ◽  
Marco Amati ◽  
Clive S. Fraser
Keyword(s):  
Satellite Imagery ◽  
Point Cloud ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Airborne Lidar ◽  
Urban Green Spaces ◽  
Cloud Classification ◽  
Tree Canopy Cover ◽  
Point Cloud Classification ◽  
Imagery Data

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Airborne LiDAR Point Cloud Classification Based on Multiple-Entity Eigenvector Fusion

2020 ◽  
Vol 47 (8) ◽  
pp. 0810002
Author(s):  
胡海瑛 Hu Haiying ◽  
惠振阳 Hui Zhenyang ◽  
李娜 Li Na
Keyword(s):  
Point Cloud ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON MULTILEVEL POINT CLUSTER FEATURES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1231-1237
Author(s):  
Y. Gao ◽  
M. C. Li
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Location Based Services ◽  
Training Data ◽  
Cloud Classification ◽  
Point Set ◽  
Point Cloud Classification

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

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