Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval
Pinglan Ge*,, Hideomi Gokon**, and Kimiro Meguro**
*Graduate School of Engineering, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
**Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
When carrying out change detection for building damage assessment using synthetic aperture radar (SAR) intensity images, it is desirable that the observation conditions of the images are similar and acquisition time is close to the earthquake occurrence time. In this way, the influence of the radar operating system and ground temporal changes can be minimized, facilitating high-accuracy assessment results. However, in practice, especially in poor developing areas, it is difficult to obtain ideal images owing to limited pre-event data archives. In the 2015 Gorkha, Nepal earthquake, the TerraSAR-X satellite captured the influenced Sankhu area before and after the earthquake on May 30, 2010 and May 13, 2015, respectively. The pre-event data was obtained in an ascending path with an incidence angle of 41°, whereas the post-event data was obtained in a descending path with an incidence angle of 33°. To apply the obtained data that had different observation conditions and longtime intervals for building damage assessment, two ways were considered and studied. On one hand, the feasibility of change detection considering these factors was investigated. Pixel statistic characteristics were analyzed in twelve test areas to check the influence of temporal changes, and building footprints were buffered considering two different incidence angles. On the other hand, the reliability of classification based on only post-event data was studied. The results showed good classification performance of some texture parameters, such as the “range value” and “standard deviation,” which are worthy of further study. Moreover, the classification results obtained using the post-event data achieved similar accuracy to that using both the pre- and post-event data, preliminarily indicating the research value of post-event data-based building damage detection as it can solve the pre-event data limitation problem once and for all.
-  M. Matsuoka and F. Yamazaki, “Characteristics of satellite images of damaged areas due to the 1995 Kobe earthquake,” 2nd Conf. on the Applications of Remote Sensing and GIS for Disaster Management, The George Washington University, 1999.
-  L. An, G. Zhang, L. Gong, and Q. Li, “Integration of SAR image and vulnerability data for building damage degree estimation,” 2016 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), pp. 4263-4266, 2016.
-  F. Wu, L. Gong, C. Wang, H. Zhang, B. Zhang, and L. Xie, “Signature analysis of building damage with TerraSAR-X new staring spotlight mode data,” IEEE Geoscience and Remote Sensing Letters, Vol.13, No.11, pp. 1696-1700, 2016.
-  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. Disaster Res., Vol.12, No.2, pp. 259-271, 2017.
-  Y. Bai, B. Adriano, E. Mas, and S. Koshimura, “Building damage assessment in the 2015 Gorkha, Nepal, earthquake using only post-event dual polarization synthetic aperture radar imagery,” Earthquake Spectra, Vol.33, No.S1, pp. 185-195, 2015.
-  M. Matsuoka and F. Yamazaki, “Building damage detection using satellite SAR intensity images for the 2003 Algeria and Iran earthquakes,” Geoscience and Remote Sensing Symp., Vol.2, pp. 1099-1102, 2004.
-  W. Wen, 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, No.S1, pp. 183-200, 2013.
-  H. Gokon, S. Koshimura, and K. Meguro, “Verification of a method for estimating building damage in extensive tsunami affected areas using L-band SAR data,” J. Disaster Res., Vol.12, No.2, pp. 251-258, 2017.
-  H. Gokon, S. Koshimura, and K. Meguro, “Towards a damage assessment in a tsunami affected area using L-band and X-band SAR data,” Joint Urban Remote Sensing Event (JURSE), pp. 1-4, 2017.
-  R. Guida, A. Iodice, and D. Riccio, “An application of the deterministic feature extraction approach to COSMO-SkyMed data,” 8th European Conf. on Synthetic Aperture Radar, pp. 1-4, 2011.
-  H. Gokon, J. Post, E. Stein, S. Martinis, A. Twele, M. Mück, C. Geiß, S. Koshimura, and M. Matsuoka, “A method for detecting buildings destroyed by the 2011 Tohoku earthquake and tsunami using multi-temporal TerraSAR-X data,” IEEE Geoscience and Remote Sensing Letters, Vol.12, No.6, pp. 1277-1281, 2015.
-  Y. Bai, B. Adriano, E. Mas, and S. Koshimura, “Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: case study of 2016 Kumamoto earthquake,” J. Disaster Res., Vol.12, No.sp, pp. 646-655, 2017.
-  M. Watanabe, R. 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, pp. 131, 2016.
-  D. Oxoli, P. Boccardo, M. Brovelli, E. Molinari, and A. Monti, “Coherent change detecting for repeated-pass interferometric SAR images: an application to earthquake damage assessment on buildings,” The Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLII-3/W4, pp. 383-388, 2018.
-  S. Chen, X. Wang, and M. Sato, “Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3.11 east japan earthquake,” The Int. Archives of the Photogrammetry, IEEE Trans. on Geoscience and Remote Sensing, Vol.54, No.12, pp. 6919-6929, 2016.
-  M. Matsuoka and F. Yamazaki, “Building damage mapping of the 2003 Bam, Iran Earthquake using Envisat/ASAR intensity imagery,” Earthquake Spectra, Vol.21, No.S1, pp. 285-294, 2005.
-  D. Brunner, K. Schulz, and T. Brehm, “Building damage assessment in decimeter resolution SAR imagery: A future perspective,” 2011 Joint Urban Remote Sensing Event, pp. 217-220, 2016.
-  W. Wagner, J. Noll, M. Borgeaud, and H. Rott, “Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer,” IEEE Trans. on Geoscience and Remote Sensing, Vol.37, No.1, pp. 206-216, 1999.
-  T. Ohsumi, Y. Mukai, and H. Fujitani, “Investigation of damage in and around Kathmandu valley related to the 2015 Gorkha, Nepal earthquake and beyond,” Geotechnical and Geological Engineering, Vol.34, No.4, pp. 1223-1245, 2016.
-  R. Guragain, “Development of earthquake risk assessment system for Nepal,” Ph.D. Thesis, The University of Tokyo, 2015.
-  HAZUS, “Multi-hazard Loss Estimation Methodology: Earthquake Model,” Department of Homeland Security, Emergency Preparedness and Response Directorate FEMA, Washington D.C., 2003.
-  FEMA 306, “Evaluation of Earthquake Damaged Concrete and Masonry Wall Buildings - Basic Procedures Manual,” prepared by the Applied Technology Council (ATC-43), 1999.
-  J. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.2, No.2, pp. 165-168, 1980.
-  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.
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