Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of t...
Guardado en:
Autores principales: | Behrokh Nazeri, Melba Crawford |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f7f77a6e54344f13aa1b7b9960399d70 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data
por: Behrokh Nazeri, et al.
Publicado: (2021) -
Assessing understory development in forest plantations using laser imaging detection and ranging (LiDAR)
por: HERNÁNDEZ,JAIME, et al.
Publicado: (2013) -
A Low-Cost, Easy-Way Workflow for Multi-Scale Archaeological Features Detection Combining LiDAR and Aerial Orthophotography
por: Antonio J. Ortiz-Villarejo, et al.
Publicado: (2021) -
Mapping tree genera using discrete LiDAR and geometric tree metrics
por: Ko,Connie, et al.
Publicado: (2012) -
PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification
por: Genping Zhao, et al.
Publicado: (2021)