Next Article in Journal
3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds
Next Article in Special Issue
An Improved Method for Power-Line Reconstruction from Point Cloud Data
Previous Article in Journal
Application of InSAR and Gravimetry for Land Subsidence Hazard Zoning in Aguascalientes, Mexico
Previous Article in Special Issue
Radiometric Block Adjustment for Multi-Strip Airborne Waveform Lidar Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(12), 17051-17076; doi:10.3390/rs71215867

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Department of Earth Observation Science, Faculty ITC, University of Twente, PO BOX 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä and Prasad S. Thenkabail
Received: 20 July 2015 / Revised: 4 December 2015 / Accepted: 8 December 2015 / Published: 17 December 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)

Abstract

The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant. View Full-Text
Keywords: building; change; classification; ALS; change detection; airborne laser scanning building; change; classification; ALS; change detection; airborne laser scanning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xu, S.; Vosselman, G.; Oude Elberink, S. Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data. Remote Sens. 2015, 7, 17051-17076.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top