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Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake
Language
English
Obiettivo Specifico
1.10. TTC - Telerilevamento
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
1/47(2009)
Publisher
IEEE
Pages (printed)
145 - 152
Issued date
January 2009
Abstract
Using satellite sensors to detect urban damage and
other surface changes due to earthquakes is gaining increasing
interest. Optical images at different resolutions and radar images
represent useful tools for this application, particularly when more
frequent revisit times will be available with the implementation
of new missions and future possible constellations of satellites.
Very high resolution (VHR) images (on the order of 1 m or
less) may provide information at the scale of a single building,
whereas images at resolutions on the order of tens of meters
may give indications of damage levels at a district scale. Both
types of information may be extremely important if provided with
sufficient timeliness to rescue teams. The earthquake that hit the
city of Bam, Iran, has been taken as a test case, where QuickBird
VHR optical images and advanced synthetic aperture radar data
were available both before and after the event. Methods to process
these data in order to detect damage and to extract features used to
estimate damage levels are investigated in this paper, pointing out
the significant potential of these satellite data and their possible
synergy.
other surface changes due to earthquakes is gaining increasing
interest. Optical images at different resolutions and radar images
represent useful tools for this application, particularly when more
frequent revisit times will be available with the implementation
of new missions and future possible constellations of satellites.
Very high resolution (VHR) images (on the order of 1 m or
less) may provide information at the scale of a single building,
whereas images at resolutions on the order of tens of meters
may give indications of damage levels at a district scale. Both
types of information may be extremely important if provided with
sufficient timeliness to rescue teams. The earthquake that hit the
city of Bam, Iran, has been taken as a test case, where QuickBird
VHR optical images and advanced synthetic aperture radar data
were available both before and after the event. Methods to process
these data in order to detect damage and to extract features used to
estimate damage levels are investigated in this paper, pointing out
the significant potential of these satellite data and their possible
synergy.
References
[1] “Preliminary observations on the Bam, Iran, earthquake of December 26,
2003,” EERI Special Earthquake Report, 2004. [Online]. Available:
http://www.eeri.org/lfe/pdf/ iran_bam_eeri_preliminary_report.pdf
[2] S. Voigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl,
“Satellite image analysis for disaster and crisis-management support,”
IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1520–1528,
Jun. 2007.
[3] H. Aoki,M.Matsuoka, and F. Yamazaki, “Characteristics of satellite SAR
images in the damaged areas due to the Hyogoken-Nanbu earthquake,” in
Proc. 19th Asian Conf. Remote Sens., 1998, vol. C7, pp. 1–6.
[4] M. Matsuoka and F. Yamazaki, “Use of satellite SAR intensity imagery
for detecting building areas damaged due to earthquakes,” Earthquake
Spectra, vol. 20, no. 3, pp. 975–994, 2004.
[5] M. Matsuoka and F. Yamazaki, “Application of the damage detection
method using SAR intensity images to recent earthquakes,” in Proc.
IGARSS, vol. 4, 2002, pp. 2042–2044.
[6] S. Stramondo, C. Bignami, M. Chini, N. Pierdicca, and A. Tertulliani,
“The radar and optical remote sensing for damage detection: Results
from different case studies,” Int. J. Remote Sens., vol. 27, pp. 4433–4447,
Oct. 20, 2006.
[7] C. Yonezawa and S. Takeuchi, “Decorrelation of SAR data by urban damage
caused by the 1995 Hoyogoken-Nanbu earthquake,” Int. J. Remote
Sens., vol. 22, no. 8, pp. 1585–1600, 2001.
[8] Y. Ito, M. Hosokawa, H. Lee, and J. G. Liu, “Extraction of damaged
regions using SAR data and neural networks,” in Proc. 19th ISPRS Congr.,
Amsterdam, The Netherlands, Jul. 16–22, 2000, vol. 33, pp. 156–163.
[9] M. Chini, C. Bignami, S. Stramondo, and N. Pierdicca, “Uplift and subsidence
due to the 26 December 2004 Indonesian earthquake detected by
SAR data,” Int. J. Remote Sens., vol. 29, no. 13, pp. 3891–3910, 2008.
[10] K. Saito, R. J. S. Spence, C. Going, and M. Markus, “Using highresolution
satellite images for post-earthquake building damage assessment:
A study following the 26 January 2001 Gujarat earthquake,”
Earthquake Spectra, vol. 20, no. 1, pp. 145–169, 2004.
[11] F. Yamakaki, M. Matsuoka, K. Kouchi, M. Kohiyama, and N. Muraoka,
“Earthquake damage detection using high-resolution satellite images,” in
Proc. IGARSS, 2004, vol. 4, pp. 2280–2283.
[12] M. Sakamoto, Y. Takasago, K. Uto, S. Kakumoto, and Y. Kosugi, “Automatic
detection of damaged area of Iran earthquake by high-resolution
satellite imagery,” in Proc. IGARSS, 2004, vol. 2, pp. 1418–1421.
[13] M. Matsuoka, T. T. Vu, and F. Yamazaki, “Automated damage detection
and visualization of the 2003 Bam, Iran, earthquake using highresolution
satellite images,” in Proc. 25th Asian Conf. Remote Sens., 2004,
pp. 841–845.
[14] F. Pacifici, F. Del Frate, C. Solimini, and W. J. Emery, “An innovative
neural-net method to detect temporal changes in high-resolution optical
satellite imagery,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 9,
pp. 2940–2952, Sep. 2007.
[15] M. Chini, F. Pacifici, W. J. Emery, N. Pierdicca, and F. Del Frate, “Comparing
statistical and neural network methods applied to very high resolution
satellite images showing changes in man-made structures at Rocky
Flats,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1812–1821,
Jun. 2008.
[16] J. Inglada and G. Mercier, “A new statistical similarity measure for change
detection in multitemporal SAR images and its extension to multiscale
change analysis,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5,
pp. 1432–1445, May 2007.
[17] Int. Centre Geohazards, “ICG reconnaissance mission, Bam earthquake
of 26 December 2003,” Oslo, Norway, ICG Report 2004-99-1, 2004.
[18] A. L. Chesnel, R. Binet, and L.Wald, “Quantitative assessment of building
damage in urban area using very high resolution images,” in Proc. Urban
Remote Sens. Joint Event, Apr. 11–13, 2007, pp. 1–5.
[19] A. L. Chesnel, R. Binet, and L. Wald, “Object oriented assessment of
damage due to natural disaster using very high resolution images,” in
Proc. IGARSS, Jul. 23–28, 2007, pp. 3736–3739.
[20] Y. Kosugi, M. Sakamoto, M. Fukunishi, W. Lu, T. Doihara, and
S. Kakumoto, “Urban change detection related to earthquakes using an
adaptive nonlinear mapping of high-resolution images,” IEEE Geosci.
Remote Sens. Lett., vol. 1, no. 3, pp. 152–156, Jul. 2004.
[21] J. A. Richards, Remote Sensing Digital Image Analysis: An Introduction.
Berlin, Germany: Springer-Verlag, 1986.
[22] M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Decision fusion for
the classification of urban remote sensing images,” IEEE Trans. Geosci.
Remote Sens., vol. 44, no. 10, pp. 2828–2838, Oct. 2006.
[23] P. Zhong and R. Wang, “Using combination of statistical models and
multilevel structural information for detecting urban areas from a single
gray-level image,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5,
pp. 1469–1482, May 2007.
[24] J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification
of hyperspectral data from urban areas based on extended morphological
profiles,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 5, pp. 480–491,
Mar. 2005.
[25] P. Soille, Morphological Image Analysis—Principles and Applications,
2nd ed. Berlin, Germany: Springer-Verlag, 2003.
[26] J. A. Benediktsson, M. Pesaresi, and K. Arnason, “Classification and
feature extraction for remote sensing images from urban areas based
on morphological transformations,” IEEE Trans. Geosci. Remote Sens.,
vol. 41, no. 9, pp. 1940–1949, Sep. 2003.
[27] F. K. Li and R. M. Goldstein, “Studies of multibaseline spaceborne interferometric
synthetic aperture radar,” IEEE Trans. Geosci. Remote Sens.,
vol. 28, no. 1, pp. 88–97, Jan. 1990.
2003,” EERI Special Earthquake Report, 2004. [Online]. Available:
http://www.eeri.org/lfe/pdf/ iran_bam_eeri_preliminary_report.pdf
[2] S. Voigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl,
“Satellite image analysis for disaster and crisis-management support,”
IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1520–1528,
Jun. 2007.
[3] H. Aoki,M.Matsuoka, and F. Yamazaki, “Characteristics of satellite SAR
images in the damaged areas due to the Hyogoken-Nanbu earthquake,” in
Proc. 19th Asian Conf. Remote Sens., 1998, vol. C7, pp. 1–6.
[4] M. Matsuoka and F. Yamazaki, “Use of satellite SAR intensity imagery
for detecting building areas damaged due to earthquakes,” Earthquake
Spectra, vol. 20, no. 3, pp. 975–994, 2004.
[5] M. Matsuoka and F. Yamazaki, “Application of the damage detection
method using SAR intensity images to recent earthquakes,” in Proc.
IGARSS, vol. 4, 2002, pp. 2042–2044.
[6] S. Stramondo, C. Bignami, M. Chini, N. Pierdicca, and A. Tertulliani,
“The radar and optical remote sensing for damage detection: Results
from different case studies,” Int. J. Remote Sens., vol. 27, pp. 4433–4447,
Oct. 20, 2006.
[7] C. Yonezawa and S. Takeuchi, “Decorrelation of SAR data by urban damage
caused by the 1995 Hoyogoken-Nanbu earthquake,” Int. J. Remote
Sens., vol. 22, no. 8, pp. 1585–1600, 2001.
[8] Y. Ito, M. Hosokawa, H. Lee, and J. G. Liu, “Extraction of damaged
regions using SAR data and neural networks,” in Proc. 19th ISPRS Congr.,
Amsterdam, The Netherlands, Jul. 16–22, 2000, vol. 33, pp. 156–163.
[9] M. Chini, C. Bignami, S. Stramondo, and N. Pierdicca, “Uplift and subsidence
due to the 26 December 2004 Indonesian earthquake detected by
SAR data,” Int. J. Remote Sens., vol. 29, no. 13, pp. 3891–3910, 2008.
[10] K. Saito, R. J. S. Spence, C. Going, and M. Markus, “Using highresolution
satellite images for post-earthquake building damage assessment:
A study following the 26 January 2001 Gujarat earthquake,”
Earthquake Spectra, vol. 20, no. 1, pp. 145–169, 2004.
[11] F. Yamakaki, M. Matsuoka, K. Kouchi, M. Kohiyama, and N. Muraoka,
“Earthquake damage detection using high-resolution satellite images,” in
Proc. IGARSS, 2004, vol. 4, pp. 2280–2283.
[12] M. Sakamoto, Y. Takasago, K. Uto, S. Kakumoto, and Y. Kosugi, “Automatic
detection of damaged area of Iran earthquake by high-resolution
satellite imagery,” in Proc. IGARSS, 2004, vol. 2, pp. 1418–1421.
[13] M. Matsuoka, T. T. Vu, and F. Yamazaki, “Automated damage detection
and visualization of the 2003 Bam, Iran, earthquake using highresolution
satellite images,” in Proc. 25th Asian Conf. Remote Sens., 2004,
pp. 841–845.
[14] F. Pacifici, F. Del Frate, C. Solimini, and W. J. Emery, “An innovative
neural-net method to detect temporal changes in high-resolution optical
satellite imagery,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 9,
pp. 2940–2952, Sep. 2007.
[15] M. Chini, F. Pacifici, W. J. Emery, N. Pierdicca, and F. Del Frate, “Comparing
statistical and neural network methods applied to very high resolution
satellite images showing changes in man-made structures at Rocky
Flats,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1812–1821,
Jun. 2008.
[16] J. Inglada and G. Mercier, “A new statistical similarity measure for change
detection in multitemporal SAR images and its extension to multiscale
change analysis,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5,
pp. 1432–1445, May 2007.
[17] Int. Centre Geohazards, “ICG reconnaissance mission, Bam earthquake
of 26 December 2003,” Oslo, Norway, ICG Report 2004-99-1, 2004.
[18] A. L. Chesnel, R. Binet, and L.Wald, “Quantitative assessment of building
damage in urban area using very high resolution images,” in Proc. Urban
Remote Sens. Joint Event, Apr. 11–13, 2007, pp. 1–5.
[19] A. L. Chesnel, R. Binet, and L. Wald, “Object oriented assessment of
damage due to natural disaster using very high resolution images,” in
Proc. IGARSS, Jul. 23–28, 2007, pp. 3736–3739.
[20] Y. Kosugi, M. Sakamoto, M. Fukunishi, W. Lu, T. Doihara, and
S. Kakumoto, “Urban change detection related to earthquakes using an
adaptive nonlinear mapping of high-resolution images,” IEEE Geosci.
Remote Sens. Lett., vol. 1, no. 3, pp. 152–156, Jul. 2004.
[21] J. A. Richards, Remote Sensing Digital Image Analysis: An Introduction.
Berlin, Germany: Springer-Verlag, 1986.
[22] M. Fauvel, J. Chanussot, and J. A. Benediktsson, “Decision fusion for
the classification of urban remote sensing images,” IEEE Trans. Geosci.
Remote Sens., vol. 44, no. 10, pp. 2828–2838, Oct. 2006.
[23] P. Zhong and R. Wang, “Using combination of statistical models and
multilevel structural information for detecting urban areas from a single
gray-level image,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5,
pp. 1469–1482, May 2007.
[24] J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification
of hyperspectral data from urban areas based on extended morphological
profiles,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 5, pp. 480–491,
Mar. 2005.
[25] P. Soille, Morphological Image Analysis—Principles and Applications,
2nd ed. Berlin, Germany: Springer-Verlag, 2003.
[26] J. A. Benediktsson, M. Pesaresi, and K. Arnason, “Classification and
feature extraction for remote sensing images from urban areas based
on morphological transformations,” IEEE Trans. Geosci. Remote Sens.,
vol. 41, no. 9, pp. 1940–1949, Sep. 2003.
[27] F. K. Li and R. M. Goldstein, “Studies of multibaseline spaceborne interferometric
synthetic aperture radar,” IEEE Trans. Geosci. Remote Sens.,
vol. 28, no. 1, pp. 88–97, Jan. 1990.
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