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Damage mapping by using object textural parameters of VHR optical data

Damage mapping by using object textural parameters of VHR optical data. C. Bignami 1 , M. Chini 1 , S. Stramondo 1 , W. J. Emery 2 , N. Pierdicca 3. 1 - Istituto Nazionale di Geofisica e Vulcanologia, Rome , Italy 2 - University of Colorado, Boulder , Colorado, USA

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Damage mapping by using object textural parameters of VHR optical data

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  1. Damage mapping by using object textural parameters of VHR optical data • C. Bignami1, M. Chini1, S. Stramondo1, W. J. Emery2, N. Pierdicca3 1 - Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy 2 - Universityof Colorado, Boulder, Colorado, USA 3 - Sapienza, UniversityofRome, Rome, Italy

  2. Presentation outline • Introduction • The test case: Bam earthquake • Available dataset: EO & ground truth • Object textural parameters approach • Results • Conclusions

  3. 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

  4. Case study 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. • Moment Mag. 6.6 • More than 25000 of human losses • Extremely heavy damage

  5. 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.

  6. 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

  7. Surveyed stations • The 7 surveyed areas superimposed on QuickBird pre-seismic image • There is also a station 8 located outside Bam, in Baravat village.

  8. 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

  9. 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) GLCM shift direction

  10. 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

  11. Contrast & damage level 1x 2x 3x W7 W15 W25

  12. Entropy & damage level 1x 2x 3x W7 W15 W25

  13. Second Moment & damage level 1x 2x 3x W7 W25 W15

  14. Homogeneity & damage level 1x 2x 3x W7 W25 W15

  15. Dissimilarity & damage level 1x 2x 3x W7 W15 W25

  16. 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

  17. 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

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