scholarly journals One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

2017 ◽  
Vol 9 (10) ◽  
pp. 1001 ◽  
Author(s):  
Zurui Ao ◽  
Yanjun Su ◽  
Wenkai Li ◽  
Qinghua Guo ◽  
Jing Zhang
Keyword(s):  
Urban Areas ◽  
Learning Algorithm ◽  
Airborne Lidar ◽  
Lidar Data ◽  
One Class Classification ◽  
Airborne Lidar Data

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Automatic LOD0 classification of airborne LiDAR data in urban and non-urban areas

2018 ◽  
Vol 51 (1) ◽  
pp. 978-990
Author(s):  
J. Balado ◽  
L. Díaz-Vilariño ◽  
P. Arias ◽  
L. M. González-Desantos
Keyword(s):  
Urban Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

Classification of Airborne Lidar Data by Echo

2012 ◽  
Vol 500 ◽  
pp. 696-700 ◽  
Author(s):  
Sheng Yao Wang ◽  
Xi Min Cui ◽  
De Bao Yuan ◽  
Jing Jing Jin ◽  
Qiang Zhang
Keyword(s):  
Data Collection ◽  
Current Data ◽  
Airborne Lidar ◽  
Classification Method ◽  
Lidar Data ◽  
Collection System ◽  
Continuous Development ◽  
Data Collection System ◽  
Airborne Lidar Data

With the continuous development of Airborne Lidar hardware, the current data collection system will not only collect information on a single echo, multiple echo information also can be available. Through the analysis and discussion of echo principle, this paper compares and elaborates the characteristics of single-echo and multiple echo information, and introduces a filter classification method based on echo information, and illustrates that the method is simple and effective according to an example.

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