A Label-Constraint Building Roof Detection Method From Airborne LiDAR Point Clouds

Airborne light detection and ranging (LiDAR) point clouds have become growingly popular as a reliable data source for 3-D digital building model reconstruction. Therefore, we develop a label-constraint approach for automatically detecting building roofs using airborne LiDAR point clouds and multispe...

Full description

Saved in:
Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2021-08, Vol.18 (8), p.1466-1470
Main Authors: Yang, Juntao, Kang, Zhizhong, Akwensi, Perpetual Hope
Format: Article
Language:English
Subjects:
Airborne light detection and ranging (LiDAR)
building roof extraction
Buildings
Detection
Dictionaries
Feature extraction
high-level semantic representation
Image reconstruction
Laser radar
Lidar
multispectral images
Neural coding
Photogrammetry
Procedures
Remote sensing
Roofs
Semantics
sparse coding
spatial consistency
Support vector machines
Task analysis
Three dimensional models
Three-dimensional displays
Visual discrimination
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Airborne light detection and ranging (LiDAR) point clouds have become growingly popular as a reliable data source for 3-D digital building model reconstruction. Therefore, we develop a label-constraint approach for automatically detecting building roofs using airborne LiDAR point clouds and multispectral images, where the label information is introduced in both the discriminative feature space generation and the detection procedure. To obtain a robust and highly discriminative descriptor, a supervised sparse coding-enhanced bag of visual word (SC-BOVW) model based on a learned discriminative dictionary is used to encode local geometric and spectral information within each super-voxel into high-level semantic representation, which is then fed into a support vector machine (SVM) classifier for distinguishing buildings from others. Additionally, a graph cut-based procedure is used as a postprocessing step to guarantee the spatial consistency in detection results. Experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark data sets. Results indicate that the proposed method is accurate and efficient in terms of building roof region detection. Moreover, the proposed method is superior to other existing methods with average differences in recall of 2.23%, precision of 0.28% and quality of 1.99%.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.2999818