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JDR Vol.11 No.3 pp. 577-592
(2016)
doi: 10.20965/jdr.2016.p0577

Paper:

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Multi-Temporal Correlation Method for Damage Assessment of Buildings from High-Resolution SAR Images of the 2013 Typhoon Haiyan

Pisut Nakmuenwai*,**, Fumio Yamazaki*, and Wen Liu*

*Graduate School of Engineering, Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

**Geo-Informatics and Space Technology Development Agency, Thailand

Received:
December 23, 2015
Accepted:
April 2, 2016
Published:
June 1, 2016
Creative Commons License
Keywords:
damage detection, multi-temporal SAR images, coherence, correlation coefficient
Abstract
In this study, damage caused by Typhoon Haiyan in the city of Tacloban, Philippines is extracted from COSMO-SkyMed imagery data. A multitemporal correlation map, i.e., a color composite of the backscattering coefficients obtained on different days and their correlation coefficients, is used to indicate changes. The Hyperboloid Change Index is proposed as a measure of the level of destruction. The method is demonstrated in a three-dimensional Cartesian coordinate system to elaborate the relationships among the aforementioned parameters. Compared to other candidate methods, a hyperboloid equation is found to be the most suitable for change detection, and its resulting positive value indicates that the typhoon had a high level of impact on the area. Potential damage areas are extracted using a thresholding operation, and the results are compared to two WorldView-2 satellite images to specifically assess coastal erosion and damage to buildings and offshore fish traps.
Cite this article as:
P. Nakmuenwai, F. Yamazaki, and W. Liu, “Multi-Temporal Correlation Method for Damage Assessment of Buildings from High-Resolution SAR Images of the 2013 Typhoon Haiyan,” J. Disaster Res., Vol.11 No.3, pp. 577-592, 2016.
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References
  1. [1] F. Adragna and J. Nicolas, “Interferometry. In Processing of Synthetic Aperture Radar Images,” H. Maitre (Ed.), John Wiley & Sons, ISTE Ltd, London, UK, pp. 279-300, 2008.
  2. [2] M. E. Engdahl and J. M. Hyyppa, “Land-Cover Classification Using Multitemporal ERS-1/2 InSAR Data,” IEEE Trans. Geosci. Remote Sens., Vol.41, 2003.
  3. [3] L. Bruzzone and A. Wiesmann, “An Advanced System for the Automatic Classification of Multitemporal SAR Images,” IEEE Trans. Geosci. Remote Sens., Vol.42, pp. 1321-1333, 2004.
  4. [4] L. Xu, S. Li, Y. Deng, and R. Wang, “Unsupervised classification of polarimetric synthetic aperture radar interferometry using polarimetric interferometric similarity parameters and SPAN,” IET Radar Sonar Navig., Vol.8, pp. 1135-1144, 2014.
  5. [5] P. Mishra and D. Singh, “A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables,” IEEE Trans. Geosci. Remote Sens., Vol.52, pp. 2889-2900, 2014.
  6. [6] J. G. Liu, A. Black, H. Lee, H. Hanaizumi, and J. Moore, “Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images,” Int. J. of Remote Sensing, Vol.22, pp. 2463-2477, 2001.
  7. [7] A. Refice, D. Capolongo, G. Pasquariello, A. D’Addabbo, F. Bovenga, R. Nutricato, F. P. Lovergine, and L. Pietranera, “SAR and InSAR for Flood Monitoring: Examples With COSMO-SkyMed Data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol.6, pp. 2711-2722, 2014.
  8. [8] D. Amitrano, G. Di Martino, A. Iodice, D. Riccio, and G. Ruello, “A New Framework for SAR Multitemporal Data RGB Representation: Rationale and Products,” IEEE Trans. Geosci. Remote Sens., Vol.1, pp. 117-133, 2015.
  9. [9] M. Preiss, D. A. Gray, and N. J. S. Stacy, “Detecting Scene Changes Using Synthetic Aperture Radar Interferometry,” IEEE Trans. Geosci. Remote Sens., Vol.44, pp. 2041-2054, 2006.
  10. [10] B. Xiong, J.M. Chen, and G. Kuang, “A change detection measure based on a likelihood ratio and statistical properties of SAR intensity images,” Remote Sensing Letters, Vol.3, pp. 267-275, 2012.
  11. [11] A. Bouaraba, A. Younsi, A. Belhadj-Aissa, M. Acheroy, N. Milisavljevic, and D. Closson, “Robust Techniques For Coherent Change Detection Using Cosmo-Skymed Sar Images,” Progress In Electromagnetics Research M 22, pp. 219-232, 2012.
  12. [12] E. J. M. Rignot and J. van Zyl, “Change Detection Techniques for ERS-1 SAR Data,” IEEE Trans. Geosci. Remote Sens., Vol.31, pp. 896-906, 1993.
  13. [13] W. Liu, F. Yamazaki, H. Gokon, and S. Koshimura, “Extraction of Tsunami-Flooded Areas and Damaged Buildings in the 2011 Tohoku-Oki Earthquake from TerraSAR-X Intensity Images,” Earthquake Spectra, Vol.29, S1, pp. S183-S200, 2013.
  14. [14] S. Stramondo, C. Bignami, M. Chini, N. Pierdicca, and A. Tertulliani, “Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies,” Int. J. Remote Sens., Vol.27, pp. 4433-4447, 2006.
  15. [15] C. Yonezawa and S. Takeuchi, “Decorrelation of SAR data by urban damages caused by the 1995 Hyogoken-nanbu earthquake,” Int. J. Remote Sens., Vol.22, pp. 1585-1600, 2001.
  16. [16] S. Takeuchi, Y. Suga, C. Yonezawa, and A. J. Chen, “Detection of Urban Disaster Using InSAR – A Case Study for the 1999 Great Taiwan Earthquake,” In Proc. of the IEEE IGARSS, Honolulu, HI, USA, 24–28 July, pp. 339-341, 2000.
  17. [17] Y. Suga, S. Takeuchi, Y. Oguro, A.J. Chen, M. Ogawa, T. Konishi, and C. Yonezawa, “Application of ERS-2/SAR data for the 1999 Taiwan earthquake,” Adv. Space Res., Vol.28, pp. 155-163, 2001.
  18. [18] M. Matsuoka and F. Yamazaki, “Characteristics of Satellite SAR Images in the Areas Damaged by Earthquakes,” In Proc. of the IEEE IGARSS, Honolulu, HI, USA, 24–28 July 2000, pp. 2693-2696.
  19. [19] D. Brunner, G. Lemoine, and L. Bruzzone, “Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery,” IEEE Trans. Geosci. Remote Sens., Vol.48, pp. 2403-2419, 2010.
  20. [20] National Disaster Risk Reduction and Management Council, Republic of the Philippines, NDRRMC Update: Updates re Effects of Typhoon Yolanda (Haiyan), 17 April 2014, http://www.ndrrmc.gov.ph/attachments/article/1329/Update_onnewline_Effects_Typhoon_YOLANDA_(Haiyan)_17APR2014.pdf [accessed Jun. 29, 2015]
  21. [21] A. M. F. Lagmay, R. P. Agaton, M. A. C. Bahala, J. B. L. T. Briones, K. M. C. Cabacaba, C. V. C. Caro, L. L. Dasallas, L. A. L. Gonzalo, C. N. Ladiero, J. P. Lapidez et al., “Devastating storm surges of Typhoon Haiyan,” Int. J. of Disaster Risk Reduction, Vol.11, pp. 1-12, 2015.
  22. [22] F. Covello, F. Battaza, A. Coletta, E. Lopinto, C. Fiorentino, L. Pietranera, , G. Valentini, and S. Zoffoli, “COSMO-SkyMed an existing opportunity for observing the Earth,” J. of Geodynamics, Vol.49, pp. 171-180, 2010.
  23. [23] The Earth Observation and Geo-Spatial Information (e-GEOS). Cosmo-Skymed Image Calibration, http://www.e-geos.it/products/pdf/COSMO-SkyMed-Image_Calibration.pdf [access Jun. 29, 2015]
  24. [24] S. Parrilli, M. Poderico, C.V. Angelino, and L. Verdoliva, “A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage,” IEEE Trans. Geosci. Remote Sens., Vol.50, No.2, pp. 606-616, 2012.
  25. [25] M. Born and E. Wolf, “Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Ligh,” 4th ed., Pergamon Press, London, England, pp. 191-554, 1970.
  26. [26] A. Ferretti, A. Monti-Guarnieri, C. Prati, and F. Rocca, “Part C InSAR processing: a mathematical approach,” In “InSAR Principles: Guidelines for SAR Interferometry Proc. and Interpretation,” K. Fletcher (Ed.), ESA Publications, Noordwijk, Netherlands, pp. 3-13, 2007.
  27. [27] F. Cigna, D. Tapete, R. Lasaponara, and N. Masini, “Amplitude change detection with Envisat ASAR to image the cultural landscape of the Nasca region, Peru,” Archaeol. Prospect., Vol.20, pp. 117-131, 2013.
  28. [28] D. Tapete, F. Cigna, N. Masini, and R. Lasaponara, “Prospection and Monitoring of the Archaeological Heritage of Nasca, Peru, with ENVISAT ASAR,” Archaeol. Prospect., Vol.20, pp. 133-147, 2013, doi: 10.1002/arp. 1449.
  29. [29] B. Aiazzi, L. Alparone, S. Baronti, and A. Garzelli, “Coherence Estimation From Multilook Incoherent SAR Imagery,” IEEE Trans. Geosci. Remote Sens., Vol.41, pp. 2531-2539, 2003.
  30. [30] Z. Chang, H. Gong, J. Zhang, and M. Chen, “Correlation Analysis on Interferometric Coherence Degree and Probability of Residue Occurrence in Interferogram,” IEEE Sensors J., Vol.14, pp. 2369-2375, 2014.
  31. [31] V. Onana, E. Trouvé, G. Mauris, J. Rudant, and E. Tonyé, “Linear Features Extraction in Rain Forest Context From Interferometric SAR Images by Fusion of Coherence and Amplitude Information,” IEEE Sensors J., Vol.41, pp. 2540-2556, 2003.
  32. [32] G. A. Arciniegas, W. Bijker, N. Kerle, and V. A. Tolpekin, “Coherence- and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data,” IEEE Trans. Geosci. Remote Sens., Vol.45, pp. 1571-1581, 2014.
  33. [33] F.C. Conesa, N. Devanthéry, A.L. Balbo, Marco. Madella, and O. Monserrat, “Use of Satellite SAR for Understanding Long-Term Human Occupation Dynamics in the Monsoonal Semi-Arid Plains of North Gujarat,” India. Remote Sens., Vol.6, No.11, pp. 11420-11443, 2014.
  34. [34] N. Kerle and R.R. Hoffman, “Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering,” Nat. Hazards Earth Syst. Sci., Vol.13, No.1, pp. 97-113, 2013.
  35. [35] L. G. Dong and J. Shan, “A comprehensive review of earthquake- induced building damage detection with remote sensing techniques,” ISPRS J. Photogramm. Remote Sens., Vol.84, pp. 85-99, 2013.
  36. [36] E. Mas, J. Bricker, S. Kure, B. Adriano, C. Yi, A. Suppasri, and S. Koshimura, “Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines,” Nat. Hazards Earth Syst. Sci., Vol.15, pp. 805-816, 2012.
  37. [37] F. Dell’Acqua and P. Gamba, “Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives,” Proc. of the IEEE., Vol.100, No.10, pp. 2876-2890, 2012.
  38. [38] M. Chini, A. Piscini, F. R. Cinti, S. Amici, R. Nappi, and P. M. de Martini, “The 2011 Tohoku (Japan) Tsunami inundation and liquefaction investigated through optical, thermal, and SAR data,” IEEE Geosci. Remote Sens. Lett., Vol.10, pp. 347-35, 2013.

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