Next Article in Journal
Retrieval of Gap Fraction and Effective Plant Area Index from Phase-Shift Terrestrial Laser Scans
Previous Article in Journal
Spectral Aging Model Applied to Meteosat First Generation Visible Band
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(3), 2572-2600; doi:10.3390/rs6032572

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

1
Section 1.4-Remote Sensing, GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany
2
Geoinformation in Environmental Planning Lab, Department of Landscape Architecture and Environmental Planning, TU Berlin, Straße des 17. Juni 145, D-10623 Berlin, Germany
*
Author to whom correspondence should be addressed.
Received: 9 October 2013 / Revised: 13 March 2014 / Accepted: 17 March 2014 / Published: 21 March 2014
View Full-Text   |   Download PDF [7502 KB, 19 June 2014; original version 19 June 2014]   |  

Abstract

Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan. View Full-Text
Keywords: co-registration; optical satellite data; multi-temporal; accuracy; Landsat; RapidEye; ASTER; SPOT; landslide; Kyrgyzstan co-registration; optical satellite data; multi-temporal; accuracy; Landsat; RapidEye; ASTER; SPOT; landslide; Kyrgyzstan
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Behling, R.; Roessner, S.; Segl, K.; Kleinschmit, B.; Kaufmann, H. Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection. Remote Sens. 2014, 6, 2572-2600.

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