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