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Damage mapping by using object textural parameters of VHR optical
data
1 - Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
2 - University of Colorado, Boulder, Colorado, USA
3 - Sapienza, University of Rome, Rome, Italy
C. Bignami1, M. Chini1, S. Stramondo1, W. J. Emery2, N. Pierdicca3
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Presentation outline
• Introduction• The test case: Bam earthquake• Available dataset: EO & ground truth• Object textural parameters approach• Results• Conclusions
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Introduction• Very high resolution (VHR) optical sensors can provide
satellite images reaching less than one meter of ground resolution
• VHR data are encouraging the development of new techniques addressing damage mapping applications
• The visual inspection is still the most reliable approach • Some efforts have been done to set up automatic
procedures• A promising technique can be based on object oriented
classification for the recognition of each building to apply change detection index at building scale
• This work presents a methodology based on textural parameters estimation for damage mapping
• An analysis of textural features sensitivity to damage level is shown
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Case study
•Moment Mag. 6.6•More than 25000 of human losses• Extremely heavy damage
On December 26, 2003 the southeastern region of Iran was hit by a strong earthquake. The epicenter was located very close to the historical city of Bam.
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Dataset description• EO data:
– Two QuickBird images were available• September 30, 2003 - Off-nadir angle: 9.7°• January 4, 2004 - Off-nadir angle: 23.8°–Higher shadow effect to be accounted for
• Panchromatic channel @ 60 cm ground resolution
• Ground truth data– Damage level based on European Macroseismic Scale 1998
(EMS98)– Ground survey by: Y. Hisada, A. Shibaya, M. R. Ghayamghamian, (2004), “Building Damage and Seismic Intensity in Bam City from the 2003 Bam, Iran, Earthquake” , Bull. Earthq. Res. Inst. Univ. Tokyo, Vol. 79 ,pp. 81-93.
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Ground truth
• Seven areas have been surveyed around seven strong motion stations
• Damage grade (EMS-98) assigned to each surveyed buildings:– Grade 1: Negligible to slight damage– Grade 2: Moderate damage– Grade 3: Substantial to heavy damage– Grade 4: Very heavy damage– Grade 5: Destruction
• Almost 400 buildings have been surveyed
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Surveyed stations
• The 7 surveyed areas superimposed on QuickBird pre-seismic image• There is also a station 8 located outside Bam, in Baravat village.
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The proposed method• Exploiting textural features (TF) for damage
mapping purposes• Instead of extracting TF by considering the gray
level co-occurrence matrix (GLCM) on a moving window, we propose to calculate the TF at object scale:– GLCM is evaluated by taking into account all
and only pixels belonging to a single object, i.e. the single building
– the actual TF of the object is derived: object textural features (OTF)
– No windows size for GLCM calculation have to be set
• 5 TFs are here presented: contrast, dissimilarity, entropy and homogeneity
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Object TF calculation• Ground survey polygons were manually drawn on the QuickBird
image• Pixels inside the polygons are used to calculate the GLCM• Pixels shift values for GLCM are 1, 2 and 3 on 135° direction (dx=dy)
shift direction
GLCM
GLCM 1 2 3 4 5 … …
1 14 7 2 7 3 … …
2 7 25 1 1 5 … …
3 2 1 12 8 9 … …
4 7 1 8 17 10 … …
5 3 5 9 10 16 … …
… … … … … … … …
… … … … … … … …
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Object TF sensitivity analysis• For each object the difference (OTF) between
post-seismic OTF (OTFpost) and pre-seismic OTF (OTFpre) has been calculated:
OTF =OTFpost - OTFpre
• mean value within a damage class has been evaluated and compared with damage level
• OTF sensitivity compared to classical moving window GLCM computation– Windows sizes
• 7x7 pixels > smaller than the smallest object• 25x25 pixels > average size of the objects• 15x15 pixels > intermediate size to compare with
previous ones– Mean TF within polygons are calculated
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Contrast & damage level1x
2x
3xW7
W25 W15
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Entropy & damage level1x
2x
3xW7
W25 W15
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Second Moment & damage level1x
2x
3xW7
W25 W15
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1x
2x
3xW7
W25W15
Homogeneity & damage level
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1x
2x
3xW7
W25 W15
Dissimilarity & damage level
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Best OTF
• Damage grade 1&2 distinguishable from 4&5 • Damage grade 3 easly to be mis-classified• Expected improvements:
– More accurate co-registration– Closer looking angle between pre and post image
-25
0
25
50
75
100
125
150
175
200
1 2 3 4 5
OTF
EMS98 damage grade
DISSIMILARITY
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Conclusions
• Textural features extraction for damage mapping purpose is presented
• TF derived for each object, i.e. the building, more robust than moving window
• Best performance from dissimilarity – 1st order TF
• Others 2nd order TF do not show good sensitivity wrt damage
• Further analysis will be performed to test anisotropy approach for GLCM