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. Bignami 1, M. Chini 1, S. Stramondo 1, W. J. Emery 2, N. Pierdicca 3

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 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, Off-nadir angle: 9.7° January 4, Off-nadir angle: 23.8° – Higher shadow effect to be accounted for Panchromatic 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

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

Object TF sensitivity analysis For each object the difference (  OTF) between post-seismic OTF (OTF post ) and pre-seismic OTF (OTF pre ) has been calculated:  OTF =OTF post - OTF pre 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 level W7 W25W15

Entropy & damage level W7 W25W15

Second Moment & damage level W7 W25 W15

W7 W25 W15 Homogeneity & damage level

W7 W25W15 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

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 – 1 st order TF Others 2 nd order TF do not show good sensitivity wrt damage Further analysis will be performed to test anisotropy approach for GLCM