Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings

  • Diego Polli
  • Fabio Dell’AcquaEmail author
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)


Seismic risk depends not only on seismic hazard, but also on the vulnerability of exposed elements since it is important in providing the necessary information to policy and decision-makers in order to prevent and mitigate the loss in lives and property. Currently, the estimation of seismic vulnerability of buildings relies on accurate, complex models to be fed with large amounts of in situ data. A limited geographical scope is a natural consequence of such approach, while extensive assessment would be desirable when risk scenarios are concerned. Remote sensing might be fruitfully exploited in this case, if not for a gap between information required by current, accurate, data-hungry vulnerability models and information derivable from remotely sensed data. In this context, naturally the greatest amount of information should be collected, and data fusion is more a necessity than an option. Fusion between optical and radar data allows covering the widest range of information pieces; in this chapter we will describe how such information may be extracted and how it can be profitably fed to simplified seismic vulnerability models to assign a seismic vulnerability class to each building. Some examples of real cases will also be presented with a special focus on the test site of Messina, Italy, a notorious seismic-prone area, where an intensive campaign of data collection is in progress within our research group.


Data fusion Very high resolution radar Building mapping Seismic vulnerability 



The authors wish to acknowledge the support of the Italian Civil Protection Department (“Programma Quadro” 2009–2011 funding of the European Centre for Training and Research in Earthquake Engineering, EUCENTRE, Pavia) and the European Commission (funding of project SAFER, 2009). They also wish to thank the colleagues at the Seismic Risk Section of EUCENTRE, particularly Helen Crowley and Barbara Borzi for their help with the SP-BELA model.


  1. 1.
    Thrower, N.J.W.: Land use in the Southwestern United States from Gemini and Apollo imagery (map suppl. no. 12). Ann. Assoc. Am. Geogr. 60(1), 208–209 (1970)Google Scholar
  2. 2.
    Myneni, R.B., Pinty, B., Maggion, D.S. Kimes, S., Iaquinta, J. Privettet, J.L., Gobron, N., Verstraetett, M., Williams, D.L.: Optical remote sensing of vegetation: modeling, caveats, and algorithms. Remote Sens. Environ. 51, 169–188 (1995)Google Scholar
  3. 3.
    Smith, R.C., Baker, K.S.: The bio-optical state of ocean waters and remote sensing. Limnol. Oceanogr. 23(2), 247–259 (1978)CrossRefGoogle Scholar
  4. 4.
    Wald, L.: A conceptual approach to the fusion of earth observation data. Surv. Geophys. 21, 177–186 (2000)CrossRefGoogle Scholar
  5. 5.
    Fonseca, L.M.G., Manjunath, B.S.: Registration techniques for multisensor remotely sensed imagery. Photogr Eng Remote Sens 62, 1049–1056 (1996)Google Scholar
  6. 6.
    Ali, M.A., Clausi, D.A.: Automatic registration of SAR and visible band remote sensing images. In: Proceedings of the Geoscience and Remote Sensing Symposium IGARSS ‘02, IEEE International, pp. 1331–1333 (2002)Google Scholar
  7. 7.
    Dare, P., Dowman, I.: A new approach to automatic feature based registration of SAR and SPOT images. Int. Arch. Photogr. Remote Sens. XXXIII, 125–130 (2000)Google Scholar
  8. 8.
    Dai, X., Khorram, S.: A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Trans. Geosci. Remote Sens. 37(5), 2351–2362 (1999)CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comp. Vis. 1(4), 321–331 (1987)CrossRefGoogle Scholar
  10. 10.
    Li, H., Manjunath, B.S., Mitra, S.K.: A contour-based approach to multisensor image registration. IEEE Trans. Image Process. 4(3), 320–334 (1995)CrossRefGoogle Scholar
  11. 11.
    Maitre, H., Wu, Y.: A dynamic programming algorithm for elastic registration of distorted pictures based on autoregressive models. IEEE Trans. Acoust. Speech Signal Process 37, 288–297 (1989)CrossRefGoogle Scholar
  12. 12.
    Hong, T.D., Schowengerdt, R.A.: A robust technique for precise registration of radar and optical satellite images. Photogr. Eng. Remote Sens. 71(5), 585–593 (2005)Google Scholar
  13. 13.
    Impagnatiello, F., Bertoni, R., Caltagirone F.: The SkyMed/COSMOsystem: SAR payload characteristics. In: Proceedings of IGARSS’98, vol. 2, pp. 689–691, 6–10 July 1998, Seattle (WA) (1998)Google Scholar
  14. 14.
    Roth, A.: TerraSAR-X: a new perspective for scientific use of high resolution spaceborne SAR data. In: Proceedings of 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 4–7, 22–23 May 2003, Berlin, Germany (2003)Google Scholar
  15. 15.
    Morena, L.C., James, K.V., Beck, J.: An introduction to the RADARSAT-2 mission. Can. J. Remote Sens. 30(3), 221–234 (2004). ISSN 1712-7971Google Scholar
  16. 16.
    Sportouche, H., Tupin, F., Denise, L.: Building extraction and 3D reconstruction in urban areas from high-resolution optical and SAR imagery. Urban Remote Sensing Event, 2009 Joint, 20–22 May, pp. 1–11 (2004)Google Scholar
  17. 17.
    Wegner, J.D., Soergel, U., Thiele, A.: Building extraction in urban scenes from high-resolution InSAR data and optical imagery. Urban Remote Sensing Event, 2009 Joint, 20–22 May, pp. 1–6 (2009)Google Scholar
  18. 18.
    Soergel, U., Thiele, A., Gross, H., Thoennessen, U.: Extraction of bridge features from high-resolution InSAR data and optical images. Urban Remote Sensing Joint Event 11–13 April 2007 pp. 1–6 (2007)Google Scholar
  19. 19.
    Stramondo, S., Bignami, C., Pierdicca, N., Chini, M.: SAR and optical remote sensing for urban damage detection and mapping: case studies. Urban Remote Sensing Joint Event, 11–13 April 2007, pp. 1–6 (2007)Google Scholar
  20. 20.
    Chini, M., Pierdicca, N., Emery, W.J.: Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam Earthquake. Geosci. Remote Sens. IEEE Trans. 47(1), Part 1, 45–152 (2009)Google Scholar
  21. 21.
    Orsomando, F., Lombardo, P., Zavagli, M., Costantini, M.: SAR and optical data fusion for change detection. Urban Remote Sensing Joint Event 11–13 pp. 1–9 (2007)Google Scholar
  22. 22.
    Zhang, J., Wang, X., Chen, T., Zhang, Y.: Change detection for the urban area based on multiple sensor information fusion. Geoscience and Remote Sensing Symposium, 2005. IGARSS ‘05. Proceedings 2005 IEEE International, vol. 1, 25–29, p 4 , July 2005Google Scholar
  23. 23.
    Calvi, G.M., Pinho, R., Bommer, J.J., Restrepo-Vélez, L.F., Crowley, H.: Development of seismic vulnerability assessment methodologies over the past 30 years. ISET J. Earthq. Technol Paper No. 472 43(3), 75–104 (2006)Google Scholar
  24. 24.
    Borzi, B., Crowley, H., Pinho, R.: Simplified pushover-based earthquake loss assessment (SP-BELA) method for masonry buildings. Int. J. Archit. Heritage 2(4), 353–376 (2008)CrossRefGoogle Scholar
  25. 25.
    Polli, D., Dell’Acqua, F., Gamba, P., Lisini, G.: Remote sensing as a tool for vulnerability assessment. In: Proceedings of the 6th International Workshop on Remote Sensing for Disaster Management Applications, Pavia, Italy, 11–12 September 2008Google Scholar
  26. 26.
    Hill, R., Moate, C., Blacknell, D.: Estimating building dimensions from synthetic aperture radar image sequences. IET Radar Sonar Navig. 2(3), 189–199 (2008)CrossRefGoogle Scholar
  27. 27.
    Bennett, A.J., Blacknell, D.: Infrastructure analysis from high resolution sar and insar imagery. In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas. Berlin, Germany (2003)Google Scholar
  28. 28.
    Cellier, F., Colin, E.: Building height estimation using fine analysis of altimetric mixtures in layover areas on polarimentric interferometric x-band sar images. In: International Geoscience and Remote Sensing Symposium (IGARSS). Denver, CO, USA (2006)Google Scholar
  29. 29.
    Simonetto, E., Oriot, H., Garello, R.: Rectangular building extraction from stereoscopic airborne radar images. IEEE Trans. Geosci. Remote Sens. 43(10), 2386–2395 (2005)CrossRefGoogle Scholar
  30. 30.
    Xu, F., Jin, Y.Q.: Automatic reconstruction of building objects from multiaspect meter-resolution sar images. IEEE Trans. Geosci. Remote Sens. 45(7), 2336–2353 (2007)CrossRefGoogle Scholar
  31. 31.
    Gamba, P., Dell’Acqua, F., Lisini, G.: BREC: the Built-up area RECognition tool. In: Proceedings of the 2009 Joint Urban Remote Sensing Event (JURSE 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Remote Sensing Group, Department of ElectronicsUniversity of PaviaPaviaItaly
  2. 2.Telecommunications and Remote Sensing Section, the European Centre for Training and Research on Earthquake Engineering (EUCENTRE)PaviaItaly

Personalised recommendations