An improved simple morphological filter for the terrain classification of airborne LIDAR data

2013 ◽  
Vol 77 ◽  
pp. 21-30 ◽  
Author(s):  
Thomas J. Pingel ◽  
Keith C. Clarke ◽  
William A. McBride
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Morphological Filter ◽  
Terrain 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

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.

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

scholarly journals TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 283-287
Author(s):  
N. Li ◽  
C. Liu ◽  
N. Pfeifer ◽  
J. F. Yin ◽  
Z.Y. Liao ◽  
...  
Keyword(s):  
Tensor Decomposition ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Observation Data ◽  
Multiple Features ◽  
Key Factor ◽  
Earth Observation Data ◽  
Airborne Lidar Data ◽  
Nearest Neighborhood

Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.

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