Open AccessTENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION
Author(s) -
N. Li,
C. Liu,
Norbert Pfeifer,
Jialai Yin,
Zhimin Liao,
Y. Zhou
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b3-283-2016
Subject(s) - raster graphics , lidar , raster data , tensor (intrinsic definition) , point cloud , computer science , artificial intelligence , pattern recognition (psychology) , representation (politics) , feature (linguistics) , component (thermodynamics) , point (geometry) , key (lock) , feature extraction , raw data , remote sensing , feature selection , data mining , mathematics , geology , physics , linguistics , philosophy , geometry , computer security , politics , political science , law , thermodynamics , programming language , pure mathematics
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.