Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-5836
Main Title: Robust automated image co-registration of optical multi-sensor time series data
Subtitle: database generation for multi-temporal landslide detection
Author(s): Behling, Robert
Roessner, Sigrid
Segl, Karl
Kleinschmit, Birgit
Kaufmann, Hermann
Type: Article
Language Code: en
Is Part Of: http://dx.doi.org/10.14279/depositonce-5525
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.
URI: http://depositonce.tu-berlin.de/handle/11303/6277
http://dx.doi.org/10.14279/depositonce-5836
Issue Date: 2014
Date Available: 13-Apr-2017
DDC Class: DDC::500 Naturwissenschaften und Mathematik::550 Geowissenschaften, Geologie::550 Geowissenschaften
Subject(s): co-registration
optical satellite data
multi-temporal
accuracy
Landsat
RapidEye
ASTER
SPOT
landslide
Kyrgyzstan
Creative Commons License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Remote sensing
Publisher: MDPI
Publisher Place: Basel
Volume: 6
Issue: 3
Publisher DOI: 10.3390/rs6032572
Page Start: 2572
Page End: 2600
EISSN: 2072-4292
Appears in Collections:Technische Universität Berlin » Fakultäten & Zentralinstitute » Fakultät 6 Planen Bauen Umwelt » Institut für Landschaftsarchitektur und Umweltplanung » Publications

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