Skip to main content
SpringerLink
Log in

A New Approach to Satellite Time-series Co-registration for Landslide Monitoring

  • Chapter
  • First Online:
Book cover Modern Technologies for Landslide Monitoring and Prediction

Part of the book series: Springer Natural Hazards ((SPRINGERNAT))

Abstract

Image-to-image co-registration is one of the preprocessing steps needed for the analysis of satellite time series. This chapter presents a new approach where all the available images are simultaneously co-registered, overcoming the limits of traditional techniques. This method was tested on the flood and landslide that occurred in Valtellina (northern Italy) during summer of 1987, resulting in the large rockslide of Val Pola. A data set made up of 13 medium-resolution satellite images collected with Landsat-4 and Landsat-5 Thematic Mapper over a period of 30 years was automatically processed. Results showed that the new approach can provide subpixel accuracy close to manual measurements, which today are considered the most accurate method for image registration. The multi-image co-registration method also demonstrated to be atmospheric resistant and robust against land-cover changes, snow, and cloud cover.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Alexander, D. (1988). Valtellina landslide and flood emergency, Northern Italy, 1987. Disasters, 12(3), 212–222.

    Article  Google Scholar 

  • Apollonio, F. I., Ballabeni, A., Gaiani, M., & Remondino, F. (2014). Evaluation of feature-based methods for automated network orientation. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 40(5), 47–54.

    Article  Google Scholar 

  • Azzoni, A., Chiesa, S., Frassoni, A., & Govi, M. (1992). The Valpola landslide. Engineering Geology, 33(1), 59–70.

    Article  Google Scholar 

  • Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). SURF: Speeded up robust features. Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1–31.

    Article  Google Scholar 

  • Barazzetti, L., Remondino, F., & Scaioni, M. (2010). Orientation and 3D modelling from markerless terrestrial images: Combining accuracy with automation. The Photogrammetric Record, 25(132), 356–381.

    Article  Google Scholar 

  • Barazzetti, L., Gianinetto, M., Scaioni, M. (2014a). Simultaneous least-squares registration of satellite time series. In: Proceedings of IEEE International Conference WHISPERS 2014 (p. 4). June 24–27 2014, Lausanne, Switzerland.

    Google Scholar 

  • Barazzetti, L., Gianinetto, M., & Scaioni, M. (2014b). Automatic co-registration of satellite time series via least squares adjustment. European Journal of Remote Sensing, 47, 55–74.

    Article  Google Scholar 

  • Bouchiha, R., & Besbes, K. (2013). Automatic remote-sensing image registration using SURF. International Journal of Computer Theory and Engineering, 5(1), 88–92.

    Article  Google Scholar 

  • Brown Gottesfeld, L. (1992). A survey of image registration techniques. ACM Computing Surveys, 24, 325–376.

    Article  Google Scholar 

  • Castro, E. D., & Morandi, C. (1987). Registration of translated and rotated images using finite Fourier transform. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 700–703.

    Article  Google Scholar 

  • Crosta, G. B., Chen, H., & Lee, C. F. (2004). Replay of the 1987 Val Pola Landslide, Italian Alps. Geomorphology, 69, 127–146.

    Article  Google Scholar 

  • Dowman, I., & Tao, V. (2002). An update on the use of rational functions for photogrammetric restitution. ISPRS Journal of Photogrammetry and Remote Sensing, 7(3), 26–29.

    Google Scholar 

  • Fiorucci, F., Cardinali, M., Carlá, R., Rossi, M., Mondini, A. C., Santurri, L., Ardizzone, F., & Guzzetti, F. (2011). Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology, 129(1–2), 50–70.

    Google Scholar 

  • Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.

    Article  Google Scholar 

  • Gianinetto, M., & Scaioni, M. (2008). Automated geometric correction of high-resolution pushbroom satellite data. Photogrammetric Engineering and Remote Sensing, 74(1), 107–116.

    Article  Google Scholar 

  • Gianinetto, M. (2012). Automatic co-registration of satellite time series. The Photogrammetric Record, 27(140), 462–470.

    Article  Google Scholar 

  • Goshtasby, A., Stockman, G. C., & Page, C. V. (1986). A region-based approach to digital image registration with subpixel accuracy. IEEE Transactions on Geoscience and Remote Sensing, 24, 390–399.

    Article  Google Scholar 

  • Gosthtasby, A. (2005). 2-D and 3-D image registration (p. 258). Hoboken, NJ, USA: Wiley.

    Google Scholar 

  • Grün, A. (2012). Development and status of image matching in photogrammetry. The Photogrammetric Record, 27, 36–57.

    Article  Google Scholar 

  • Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112, 42–66.

    Article  Google Scholar 

  • Heid, T., & Kääb, A. (2012). Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery. Remote Sensing of Environment, 118, 339–355.

    Article  Google Scholar 

  • Khairunizza-Bejo, S., & Petrou, M. (2010). Elastic image registration for landslide monitoring. International Journal of Signal Processing, 3(3), 71–86.

    Google Scholar 

  • Kraus, K. (1997). Photogrammetry. Volume 2. Advanced methods and applications (p. 466). Bonn: Dümmler Verlag.

    Google Scholar 

  • Kraus, K. (2008). Photogrammetry: Geometry from images and laser scans (p. 459). Germany: Walter de Gruyter.

    Google Scholar 

  • Le Moigne, J., Netanyahu, N. S., & Eastman, R. D. (2011). Image registration for remote sensing (p. 484). UK: Cambridge University Press.

    Book  Google Scholar 

  • Maianti, P., Rusmini, M., Tortini, R., Dalla Via, G., Frassy, F., Marchesi, A., Rota Nodari, F., & Gianinetto, M. (2014). Monitoring large oil slick dynamics with moderate resolution multispectral satellite data. Natural Hazards, 73, 473–492.

    Google Scholar 

  • Mantovani, F., Soeters, R., & Van Westen, C. J. (1996). Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology, 15(2), 213–225.

    Article  Google Scholar 

  • Metternicht, G., Hurni, L., & Gogu, R. (2005). Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment, 98, 284–303.

    Article  Google Scholar 

  • Mikhail, E. M., Bethel, J. S., & McGlone, J. C. (2001). Introduction to modern photogrammetry (p. 479). New York: Wiley.

    Google Scholar 

  • Nayak, S., & Zlatanova, S. (2008). Remote sensing and GIS technologies for monitoring and prediction of disasters (p. 271). Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  • Poli, D., & Toutin, T. (2012). Review of developments in geometric modelling for high resolution satellite pushbroom sensors. The Photogrammetric Record, 27(137), 58–73.

    Article  Google Scholar 

  • Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2001). Mutual information matching in multiresolution contexts. Image and Vision Computing, 19, 45–52.

    Article  Google Scholar 

  • Pratt, W. K. (1991). Digital image processing (2nd ed.). New York: Wiley.

    Google Scholar 

  • Price, K. E. (1985). Relaxation matching techniques—a comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 617–623.

    Article  Google Scholar 

  • Qiao, G., Lu, P., Scaioni, M., Xu, S., Tong, X., Feng, T., et al. (2013). Landslide investigation with remote sensing and sensor network: From susceptibility mapping and scaled-down simulation towards in situ sensor network design. Remote Sensing, 5(9), 4319–4346.

    Article  Google Scholar 

  • Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014a). Remote sensing for landslide investigations: An overview on recent achievements and perspectives. Remote Sensing, 6(10), 9600–9652. doi:10.3390/rs6109600.

  • Scaioni, M., Feng, T., Lu, P., Qiao, G., Tong, X., Li, R., et al. (2014b). Close-range photogrammetric techniques for deformation measurement: Applications to landslides. In M. Scaioni (Ed.), Modern technologies for landslide investigation and prediction (pp. 13–41). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Smith, D. P., & Atkinson, S. F. (2001). Accuracy of rectification using topographic map versus ground control points. Photogrammetric Engineering and Remote Sensing, 67(5), 565–570.

    Google Scholar 

  • Teke, M., & Temizel, A. (2010). Multi-spectral Satellite image registration using scale-restricted SURF. In: Proceedings of 20th International Conference on Pattern Recognition (pp. 2310–2313). August 23–26 2010, Istanbul, Turkey.

    Google Scholar 

  • Wahed, M., El-tawel, G. S., & Gad El-karim, A. (2013). Automatic image registration technique of remote sensing images. International Journal of Advanced Computer Science and Applications, 4(2), 177–187.

    Google Scholar 

  • Yang, C. J. L., Ren, X. L., & Huang, H. (2012). The vegetation damage assessment of the Wenchuan earthquake of May 2008 using remote sensing and GIS. Natural Hazards, 62, 45–55.

    Article  Google Scholar 

  • Zlatanova, S., & Li, J. (2008). Geospatial information technology for emergency response (p. 381). London: Taylor & Francis Group.

    Google Scholar 

Download references

Acknowledgments

This work has been supported by the Italian Ministry of Education, University and Research (MIUR) within the grant FIRB 2010 entitled: ‘Subpixel techniques for matching, image registration and change detection with applications to civil and environmental engineering’ (No. RBFR10NM3Z). Acknowledgements also go to the 973 National Basic Research Program of China 973 (No. 2013CB733204).

Author information

Authors and Affiliations

  1. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, via Ponzio 31, 20133, Milan, Italy

    Luigi Barazzetti, Marco Gianinetto & Marco Scaioni

  2. College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai, 200092, People’s Republic of China

    Marco Scaioni

Authors
  1. Luigi Barazzetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Gianinetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Scaioni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luigi Barazzetti .

Editor information

Editors and Affiliations

  1. College of Surveying & Geo-Informatics, Department of Architecture, Built Environment and Construction Engineering,, Tongji University, Shanghai, Politecnico di Milano, Italy, China

    Marco Scaioni

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Barazzetti, L., Gianinetto, M., Scaioni, M. (2015). A New Approach to Satellite Time-series Co-registration for Landslide Monitoring. In: Scaioni, M. (eds) Modern Technologies for Landslide Monitoring and Prediction. Springer Natural Hazards. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45931-7_12

Download citation

Publish with us

Policies and ethics

Search

Navigation