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JDR Vol.12 No.sp pp. 646-655
doi: 10.20965/jdr.2017.p0646
(2017)

Paper:

Machine Learning Based Building Damage Mapping from the ALOS-2/PALSAR-2 SAR Imagery: Case Study of 2016 Kumamoto Earthquake

Yanbing Bai*,†, Bruno Adriano**, Erick Mas** and Shunichi Koshimura**

*Graduate School of Engineering, Tohoku University
6-6-4 Aramaki-Aza Aoba, Aob-ku, Senda, Miyagi 980-8579, Japan

Corresponding author

**International Research Institute of Disaster Science, Tohoku University, Miyagi, Japan

Received:
December 28, 2016
Accepted:
June 20, 2017
Published:
June 30, 2017
Keywords:
2016 Kumamoto earthquake, building damage mapping, ALOS-2/PALSAR-2, synthetic aperture radar, machine learning
Abstract

Synthetic Aperture Radar (SAR) remote sensing is a useful tool for mapping earthquake-induced building damage. A series of operational methodologies based on SAR data using either multi-temporal or only post-event SAR images have been developed and used to serve disaster activities. This presents a critical problem: which method is more likely to obtain reliable results and should be adopted for disaster response when both pre- and post-event SAR data are available? To explore this question, this study takes the 2016 Kumamoto earthquake as a case study. ALOS-2/PALSAR-2 SAR images were employed with a machine learning framework to quantitatively compare the performance of building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images. The results show that an overall accuracy of 64.5% was achieved when only post-event SAR images were used, which is 2.3% higher than the overall accuracy when multi-temporal SAR images were used. The estimated building damage ratio for the former and the latter are 29.7% and 31.1%, respectively, which are both close to the building damage ratio obtained from an optical image.

References
  1. [1] S.-H. Yun, K. Hudnut, S. Owen, F. Webb, M. Simons, P. Sacco, E. Gurrola, G. Manipon, C. Liang, E. Fielding, P. Milillo, H. Hua, and A. Coletta, “Rapid Damage Mapping for the 2015 M w 7.8 Gorkha Earthquake Using Synthetic Aperture Radar Data from COSMO-SkyMed and ALOS-2 Satellites,” Seismological Research Letters, Vol.86, No.6, pp. 1549-1556, 2015.
  2. [2] A. O. F. Geophysics, F. Track, and I. Nazionale, “A multisensor approach for the 2016 Amatrice earthquake damage assessment,” No.1981, pp. 1-6, 2016.
  3. [3] L. Ge, A. H.-M. Ng, X. Li, Y. Liu, Z. Du, and Q. Liu, “Near real-time satellite mapping of the 2015 Gorkha earthquake, Nepal,” Annals of GIS, Vol.21, No.3, pp. 175-190, 2015.
  4. [4] T. Balz and M. S. Liao, “Building-damage detection using postseismic high-resolution SAR satellite data,” Int. J. of Remote Sensing, Vol.31, No.13, pp. 3369-3391, 2010.
  5. [5] M. Matsuoka and F. Yamazaki, “Characteristics of satellite SAR images in the areas damaged by earthquakes,” Int. Geoscience and Remote Sensing Symposium (IGARSS), Vol.6, pp. 2693-2696, 2000.
  6. [6] M. Matsuoka and F. Yamazaki, “Building damage detection using satellite SAR intensity images for the 2003 Algeria and Iran earthquakes,” Geoscience and Remote Sensing Symposium, Vol.2, pp. 1099-1102, 2004.
  7. [7] M. Matsuoka and F. Yamazaki, “Building damage detection using SAR intensity images for recent earthquakes,” European Space Agency, (Special Publication) ESA SP, No.572, pp. 2021-2026, 2005.
  8. [8] M. Matsuoka and M. Estrada, “Development of earthquake-induced building damage estimation model based on ALOS/PALSAR observing the 2007 Peru earthquake,” J. of Disaster Research, Vol.8, No.2, pp. 346–355, 2013.
  9. [9] W. Liu, F. Yamazaki, and T. Sasagawa, “Monitoring of the Recovery Process of the Fukushima Daiichi Nuclear Power Plant from VHR SAR Images,” Vol.11, No.2, 2016.
  10. [10] F. Yamazaki, Y. Iwasaki, W. Liu, T. Nonaka, and T. Sasagawa, “Detection of damage to building side-walls in the 2011 Tohoku, Japan earthquake using high-resolution TerraSAR-X images,” Vol.8892, pp. 1-9, 2013.
  11. [11] S. Plank, “Rapid damage assessment by means of multi-temporal SAR-A comprehensive review and outlook to Sentinel-1,” Remote Sensing, Vol.6, pp. 4870-4906, 2014.
  12. [12] H. Miura, S. Midorikawa, and M. Matsuoka, “Building Damage Assessment Using High-Resolution Satellite SAR Images of the 2010 Haiti Earthquake,” Earthquake Spectra, Vol.32, No.1, pp. 591-610, 2015.
  13. [13] L. Dong and J. Shan, “A comprehensive review of earthquake-induced building damage detection with remote sensing techniques,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.84, pp. 85-99, 2013.
  14. [14] R. Guida, A. Iodice, and D. Riccio, “Monitoring of collapsed builtup areas with high resolution SAR images,” Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE Int., pp. 2422-2425, 2010.
  15. [15] S.-w. Chen, S. Member, and M. Sato, “Tsunami Damage Investigation of Built-Up Areas Using Multi-temporal Spaceborne Full Polarimetric SAR Images,” Vol.51, No.4, pp. 1985-1997, 2013.
  16. [16] J. Hoffmann, “Mapping damage during the Bam (Iran) earthquake using interferometric coherence,” Int. J. of Remote Sensing, Vol.28, No.6, pp. 1199-1216, 2007.
  17. [17] 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. on Geoscience and Remote Sensing, Vol.45, No.6, pp. 1571-1581, 2007.
  18. [18] M. Watanabe, R. B. Thapa, T. Ohsumi, H. Fujiwara, C. Yonezawa, N. Tomii, and S. Suzuki, “Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2,” Earth, Planets and Space, Vol.68, No.1, p. 131, 2016.
  19. [19] W. Zhai and C. Huang, “Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake,” Earth, Planets and Space, Vol.68, No.1, p. 86, 2016.
  20. [20] X. Li, H. Guo, L. Zhang, X. Chen, and L. Liang, “A New Approach to Collapsed Building Extraction Using RADARSAT-2 Polarimetric SAR Imagery,” Vol.9, No.4, pp. 677-681, 2012.
  21. [21] L. Shi, W. Sun, J. Yang, P. Li, and L. Lu, “Building Collapse Assessment by the Use of Postearthquake Chinese VHR Airborne SAR,” IEEE Geoscience and Remote Sensing Letters, Vol.12, No.10, pp. 2021-2025, 2015.
  22. [22] L. Gong, C. Wang, F. Wu, J. Zhang, H. Zhang, and Q. Li, “Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery,” Remote Sensing, Vol.8, No.12, p. 887, 2016.
  23. [23] Y. Hata, H. Goto, and M. Yoshimi, “Preliminary Analysis of Strong Ground Motions in the Heavily Damaged Zone in Mashiki Town, Kumamoto, Japan, during the Mainshock of the 2016 Kumamoto Earthquake (Mw7.0) Observed by a Dense Seismic Array,” Seismological Research Letters, Vol.87, No.5, pp. 1044-1049, 2016.
  24. [24] Map data Imagery@ Google, 2016, https://www.google.co.jp/maps [accessed December 1,2016]
  25. [25] F. Dell’Acqua, C. Bignami, M. Chini, G. Lisini, D. A. Polli, and S. Stramondo, “Earthquake damages rapid mapping by satellite remote sensing data: L’Aquila april 6th, 2009 event,” IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.4, No.4, pp. 935-943, 2011.
  26. [26] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software,” ACM SIGKDD Explorations Newsletter, Vol.11, No.1, p. 10, 2009.
  27. [27] ENVI/SARscape, http://sarmap.ch/tutorials/Basic.pdf [accessed May 1, 2016]
  28. [28] Sentinel Toolboxes, 2016, https://sentinel.esa.int/web/sentinel/toolboxes [accessed May 12, 2016]
  29. [29] A. B. Owen, “Infinitely Imbalanced Logistic Regression,” J. of Machine Learning Research, No.1, pp. 1-13, 2006.
  30. [30] H. He and E. Garcia, “Learning from Imbalanced Data Sets.,” IEEE Trans. on knowledge and data engineering, Vol.21, No.9, pp. 1263-1264, 2010.
  31. [31] L. Zeng, “Logistic Regression in Rare Events Data,” Political Analysis Vol.9, Issue 2, pp. 137-163, 2016.
  32. [32] R. Blagus and L. Lusa, “Class prediction for high-dimensional class-imbalanced data,” BMC Bioinformatics, Vol.11, No.1, p. 523, 2010.
  33. [33] J. Burez and D. Van den Poel, “Handling class imbalance in customer churn prediction,” Expert Systems with Applications, Vol.36, No.3 PART 1, pp. 4626-4636, 2009.
  34. [34] M. Kajimoto and J. Susaki, “Urban Density Estimation From Polarimetric SAR Images Based on a POA Correction Method,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.6, Issue: 3, pp. 1418-1429, 2013.
  35. [35] Y. Yamaguchi, “Four-component scattering power decomposition using coherency matrix,” Int. Geoscience and Remote Sensing Symposium (IGARSS), Vol.49, No.6, pp. 1044-1047, 2011.
  36. [36] Y. Bai, B. Adriano, E. Mas, H. Gokon, and S. Koshimura, “Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images,” J. of Disaster Research, Vol.12, No.2, p. 259, 2017.
  37. [37] R. M. Haralick and K. Shanmugam, “Textural features for image classification,” IEEE Trans. on systems, Man, and Cybernetics, Vol.SMC-3, Issue 6, pp. 610-621, 1973.
  38. [38] A. B. Girisha, M. C. Chandrashekhar, and D. M. Kurian, “FPGA implementation of GLCM,” Int. J. of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol.2, Issue 6, 2013.

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Last updated on Oct. 20, 2017