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

Remote Sensing

Robert Behling¹  Sigrid Roessner¹  Karl Segl¹  Birgit Kleinschmit² 

[1] Section 1.4-Remote Sensing, GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany; E-Mails:;Geoinformation in Environmental Planning Lab, Department of Landscape Architecture and Environmental Planning, TU Berlin, Straße des 17. Juni 145, D-10623 Berlin, Germany; E-Mail:

关键词: co-registration;    optical satellite data;    multi-temporal;    accuracy;    Landsat;    RapidEye;    ASTER;    SPOT;    landslide;    Kyrgyzstan;   

DOI  :  10.3390/rs6032572

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland