Next Article in Journal
Remote Sensing Analysis Techniques and Sensor Requirements to Support the Mapping of Illegal Domestic Waste Disposal Sites in Queensland, Australia
Next Article in Special Issue
Lateral Offset Quality Rating along Low Slip Rate Faults: Application to the Alhama de Murcia Fault (SE Iberian Peninsula)
Previous Article in Journal
Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia)
Previous Article in Special Issue
To Fill or Not to Fill: Sensitivity Analysis of the Influence of Resolution and Hole Filling on Point Cloud Surface Modeling and Individual Rockfall Event Detection
Article Menu

Export Article

Erratum published on 15 December 2015, see Remote Sens. 2015, 7(12), 16915-16916.

Open AccessArticle
Remote Sens. 2015, 7(10), 13029-13052; doi:10.3390/rs71013029

A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner

Department of Geological Sciences and Geological Engineering, Queen's University, 36 Union Street, Kingston, ON K7L 3N6, Canada
Risk Analysis Group, Institute of Earth Sciences, University of Lausanne, CH-1015 Lausanne, Switzerland
BGC Engineering, 414 Princeton Ave., Ottawa, ON K2A 1B5, Canada
Canadian National Railway, 10229–127 Avenue, Edmonton, AB T5E 0B9, Canada
Author to whom correspondence should be addressed.
Academic Editors: Marc-Henri Derron, Richard Müller and Prasad S. Thenkabail
Received: 10 July 2015 / Accepted: 24 August 2015 / Published: 1 October 2015
(This article belongs to the Special Issue Use of LiDAR and 3D point clouds in Geohazards)
View Full-Text   |   Download PDF [8189 KB, uploaded 8 January 2016]   |  


This study presents a point cloud de-noising and calibration approach that takes advantage of point redundancy in both space and time (4D). The purpose is to detect displacements using terrestrial laser scanner data at the sub-mm scale or smaller, similar to radar systems, for the study of very small natural changes, i.e., pre-failure deformation in rock slopes, small-scale failures or talus flux. The algorithm calculates distances using a multi-scale normal distance approach and uses a set of calibration point clouds to remove systematic errors. The median is used to filter distance values for a neighbourhood in space and time to reduce random type errors. The use of space and time neighbours does need to be optimized to the signal being studied, in order to avoid smoothing in either spatial or temporal domains. This is demonstrated in the application of the algorithm to synthetic and experimental case examples. Optimum combinations of space and time neighbours in practical applications can lead to an improvement of an order or two of magnitude in the level of detection for change, which will greatly improve our ability to detect small changes in many disciplines, such as rock slope pre-failure deformation, deformation in civil infrastructure and small-scale geomorphological change. View Full-Text
Keywords: point cloud; de-noising; LiDAR; Terrestrial Laser Scanning; monitoring; change detection point cloud; de-noising; LiDAR; Terrestrial Laser Scanning; monitoring; change detection

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).

Supplementary material

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

Kromer, R.A.; Abellán, A.; Hutchinson, D.J.; Lato, M.; Edwards, T.; Jaboyedoff, M. A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner. Remote Sens. 2015, 7, 13029-13052.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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