Paper:
Identifying Building Damage Patterns in the 2016 Meinong, Taiwan Earthquake Using Post-Event Dual-Polarimetric ALOS-2/PALSAR-2 Imagery
Yanbing Bai†, Bruno Adriano, Erick Mas, and Shunichi Koshimura
International Research Institute of Disaster Science (IRIDeS), Tohoku University
Aoba 6-6-03, Sendai 980-8579, Japan
†Corresponding author
The 2016 magnitude 6.4 Meinong earthquake caused catastrophic damage to peoples lives and properties in Taiwan. Synthetic Aperture Radar remote sensing is a useful tool to rapidly grasp the near real-time building damage to areas affected by the earthquake. Previous studies employed X-band single polarized high-resolution synthetic aperture radar imagery to identify building damage. However, suitable X-band single polarized high-resolution synthetic aperture radar imagery is not always accessible. Therefore, this research applied L-band dual-polarimetric ALOS-2/PALSAR-2 data to analyze the radar scattering characteristics of three types of affected buildings in the 2016 Meinong earthquake. The results show that collapsed buildings are characterized by a weak double-bounce scattering due to a reduced dihedral structure, while the characteristics of slightly damaged buildings are similar to those of undamaged buildings. Furthermore, the discrimination ability of a series of polarimetric, texture, and color features derived from the dual-polarimetric SAR data for three types of buildings affected by the earthquake are quantified based on a statistical analysis using the pixels in the combined areas of layover, shadow, and building footprint of each building. The results of the statistical analysis show that the spaceborne dual-polarimetric ALOS-2/PALSAR-2 images have good potential to distinguish between slightly damaged buildings and collapsed and tilted buildings. However, it is still difficult to distinguish between collapsed and tilted buildings. In addition, the results of the statistical analysis show that the mean value and variance value of the Gray-Level Co-Occurrence Matrix of the span image are two suitable features by which the categories of building damage can be distinguished. The polarimetric and color features demonstrated poorer performance in terms of distinguishing between damage categories than the texture features.
- [1] Y.-T. Lee, Y.-J. Wang, C.-H. Chan, and K.-F. Ma, “The 2016 Meinong Earthquake to TEM PSHA2015,” Terr. Atmos. Ocean. Sci., 2017.
- [2] 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.
- [3] W. Liu, M. Matsuoka, F. Yamazaki, T. Nonaka, and T. Sasagawa, “Detection of Building Side-Wall Damage Caused By the 2011 Tohoku , Japan Earthquake Tsunamis Using High-Resolution Sar Imagery,” Proceedings of the 10th National Conference in Earthquake Engineering, 2014.
- [4] W. Liu, F. Yamazaki, and T. Sasagawa, “Monitoring of the recovery process of the fukushima daiichi nuclear power plant from vhr sar images,” Journal of Disaster Research, Vol.11, No.2, p. 237, 2016.
- [5] 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.4, No.4, pp. 935–943, 2011.
- [6] L. Moya, L. R. Marval Perez, E. Mas, B. Adriano, S. Koshimura, and F. Yamazaki, “Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery,” Hazard Scenarios and Fragility Functions. Remote Sensing, Vol.10, No.2, p. 296, 2018.
- [7] Y. Bai, et al. “Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post-Event Dual Polarization Synthetic Aperture Radar Imagery,” Earthquake Spectra, Vol.33.S1, pp. S185-S195, 2017.
- [8] M. Matsuoka and M. Estrada, “Development of earthquake-induced building damage estimation model based on ALOS/PALSAR observing the 2007 Peru earthquake,” Journal of Disaster Research, Vol.8, No.2, pp. 346–355, 2013.
- [9] N. M. P. Jaya, M. Fusanori, and A. B. Rimba, “Estimation of damaged areas due to the 2010 chile earthquake and tsunami using sar imagery of alos/palsar,” ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol.3, No.8, 2016.
- [10] S. Karimzadeh and M. Mastuoka, “Building damage assessment using multisensor dual-polarized synthetic aperture radar data for the 2016 m 6.2 amatrice earthquake, italy,” Remote Sensing, Vol.9, No.4, p. 330, 2017.
- [11] 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.
- [12] X. Li, H. Guo, L. Zhang, X. Chen, and L. Liang, “A new approach to collapsed building extraction using radarsat-2 polarimetric sar imagery,” IEEE Geoscience and Remote Sensing Letters, Vol.9, No.4, pp. 677–681, 2012.
- [13] 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.4, No.4, pp. 935–943, 2011.
- [14] T. Balz and M. Liao, “Building-damage detection using post-seismic high-resolution sar satellite data,” International Journal of Remote Sensing, Vol.31, No.13, pp. 3369–3391, 2010.
- [15] M. Sato, S.-W. Chen, and M. Satake, “Polarimetric sar analysis of tsunami damage following the march 11, 2011 east japan earthquake,” Proceedings of the IEEE, Vol.100, No.10, pp. 2861–2875, 2012.
- [16] Y. Yamaguchi, “Disaster monitoring by fully polarimetric sar data acquired with alos-palsar,” Proceedings of the IEEE, Vol.100, No.10, pp. 2851–2860, 2012.
- [17] J. S. et al., “2016 meinong, taiwan earthquake,” http://www.geerassociation.org/component/geer_reports/?view=geerreports&id=73 [accessed Mar 14, 2016]
- [18] M. Shimada, O. Isoguchi, T. Tadono, and K. Isono, “Palsar radiometric and geometric calibration,” IEEE Transactions on Geoscience and Remote Sensing, Vol.47, No.12, pp. 3915–3932, 2009.
- [19] 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.
- [20] 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, No.3, pp. 1418–1429, 2013.
- [21] D. Xiang, T. Tang, Y. Ban, and Y. Su, “Man-made target detection from polarimetric sar data via nonstationarity and asymmetry,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.9, No.4, pp. 1459–1469, 2016.
- [22] L. Zhao, J. Yang, P. Li, L. Zhang, L. Shi, and F. Lang, “Damage assessment in urban areas using post-earthquake airborne polsar imagery,” International journal of remote sensing, Vol.34, No.24, pp. 8952–8966, 2013.
- [23] S.-W. Chen, X.-S. Wang, Y.-Z. Li, and M. Sato, “Adaptive model-based polarimetric decomposition using polinsar coherence,” IEEE Transactions on Geoscience and Remote Sensing, Vol.52, No.3, pp. 1705–1718, 2014.
- [24] Y. Yamaguchi, A. Sato, W.-M. Boerner, R. Sato, and H. Yamada, “Four-component scattering power decomposition with rotation of coherency matrix,” IEEE Transactions on Geoscience and Remote Sensing, Vol.49, No.6, pp. 2251–2258, 2011.
- [25] M.-A. Moen, “Analysis and interpretation of c-band polarimetric sar signatures of sea ice,” 2015.
- [26] P. T. B. Brett and R. Guida, “Earthquake damage detection in urban areas using curvilinear features,” IEEE Transactions on Geoscience and Remote Sensing, Vol.51, No.9, pp. 4877–4884, 2013.
- [27] 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, 2016.
- [28] T. Ainsworth, R. Jansen, J. Lee, and R. Fiedler, “Sub-aperture analysis of high-resolution polarimetric sar data,” in Geoscience and Remote Sensing Symposium, 1999. IGARSS’99 Proceedings. IEEE 1999 International, Vol.1, pp. 41–43, IEEE, 1999.
- [29] C. Fang, H. Wen, and W. Yirong, “An improved cloude-pottier decomposition using h/α/span and complex wishart classifier for polarimetric sar classification,” in Radar, 2006. CIE’06. International Conference on, pp. 1–4, IEEE, 2006.
- [30] R. M. Haralick, K. Shanmugam, et al., “Textural features for image classification,” IEEE Transactions on systems, man, and cybernetics, No.6, pp. 610–621, 1973.
- [31] S. Uhlmann and S. Kiranyaz, “Classification of dual-and single polarized sar images by incorporating visual features,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.90, pp. 10–22, 2014.
- [32] D. Brunner, G. Lemoine, and L. Bruzzone, “Earthquake damage assessment of buildings using vhr optical and sar imagery,” IEEE Transactions on Geoscience and Remote Sensing, Vol.48, No.5, pp. 2403–2420, 2010.
- [33] F. Dell’Acqua, G. Lisini, and P. Gamba, “Experiences in optical and sar imagery analysis for damage assessment in the wuhan, may 2008 earthquake,” in Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, Vol.4, pp. IV–37, IEEE, 2009.
- [34] W. Liu, F. Yamazaki, B. Adriano, E. Mas, and S. Koshimura, “Development of building height data in peru from high-resolution sar imagery,” Journal of Disaster Research, Vol.9, No.6, pp. 1042–1049, 2014.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.