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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
Citation preview
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
Presentation outline
• Introduction• The test case: Bam earthquake• Available dataset: EO & ground truth• Object textural parameters approach• Results• Conclusions
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
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.
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.
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
Surveyed stations
• The 7 surveyed areas superimposed on QuickBird pre-seismic image• There is also a station 8 located outside Bam, in Baravat village.
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
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 … …
… … … … … … … …
… … … … … … … …
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
Contrast & damage level1x
2x
3xW7
W25 W15
Entropy & damage level1x
2x
3xW7
W25 W15
Second Moment & damage level1x
2x
3xW7
W25 W15
1x
2x
3xW7
W25W15
Homogeneity & damage level
1x
2x
3xW7
W25 W15
Dissimilarity & damage level
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
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