LiDAR point classification based on joint sparse representation in kernel space

Resent years, sparse representation theory has been widely used in signal processing field. Researchers introduce this theory into the application of pattern recognition and classification and get the sparse representation classifier (SRC). In this paper, we use the SRC to achieve the classification...

Full description

Saved in:
Bibliographic Details
Main Authors: Bingqian Xie, Yanfeng Gu, Qingwang Wang, Huan Liu
Format: Conference Proceeding
Language:English
Subjects:
Buildings
Feature extraction
Joint sparse representation
Kernel
kernel method
Laser radar
LiDAR point classification
Three-dimensional displays
Training
Vegetation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Resent years, sparse representation theory has been widely used in signal processing field. Researchers introduce this theory into the application of pattern recognition and classification and get the sparse representation classifier (SRC). In this paper, we use the SRC to achieve the classification of LiDAR (Light Detection and Ranging) points. To get a better performance, we introduce the kernel method into SRC, for the advancement of kernel in solving nonlinear problem. Also, a joint sparse representation is used for the category similarity of neighboring LiDAR points.
ISSN:2153-7003
DOI:10.1109/IGARSS.2016.7729377