JDR Vol.12 No.2 pp. 259-271
doi: 10.20965/jdr.2017.p0259


Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images

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

*Graduate School of Engineering, Tohoku University
Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845, Japan

Corresponding author

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

***Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

October 4, 2016
February 4, 2017
Online released:
March 16, 2017
March 20, 2017
2015 Nepal earthquake, object-based building damage assessment, post-event dual-polarimetric SAR imagery, Random Forest machine learning algorithms
Earthquake-induced building damage assessment is an indispensable prerequisite for disaster impact assessment, and the increasing availability of high-resolution Synthetic Aperture Radar (SAR) imagery has made it possible to construct damaged building inventories soon after earthquakes strike. However, the shortage of pre-seismic SAR datasets and the lack of available building footprint data pose challenges for rapid building damage assessment. Taking advantage of recent advances in machine learning algorithms, this study proposes an object-based building damage assessment methodology that uses only post-event SAR imagery. A Random Forest machine learning-based object classification, a simplified approach to the extraction of built-up areas, was developed and tested on two ALOS2/PALSAR-2 dual polarimetric SAR images acquired in affected areas soon after the 2015 Nepal earthquake. In addition, a series of texture metrics as well as the random scattering metric and reflection symmetry metric were found to significantly enhance classification accuracy. The feature selection was found to have a positive effect on overall performance. Moreover, the proposed Random Forest framework resulted in overall accuracies of 93% with a kappa coefficient of 0.885 when the object scale of 60 × 60 pixels and 15 features were adopted. A comparative experiment with the k-nearest neighbor framework demonstrated that the Random Forest framework is a significant step toward the achievement of a balanced, two-class classification.
Cite this article as:
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.
Data files:
  1. [1] S. A. Bartels and M. J. Vanrooyen, “Medical complications associated with earthquakes,” 2012.
  2. [2] M. Dou, J. Chen, D. Chen, X. Chen, Z. Deng, X. Zhang, K. Xu, and J. Wang, “Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images,” Future Generation Computer Systems, Vol.37, pp. 367–377, 2014.
  3. [3] K. Saito, R. J. S. Spence, C. Going, and M. Markus, “Using High-Resolution Satellite Images for Post-Earthquake Building Damage Assessment: A Study Following the 26 January 2001 Gujarat Earthquake,” 2004.
  4. [4] F. Yamazaki, Y. Yano, and M. Matsuoka, “Visual damage interpretation of buildings in Bam city using QuickBird images following the 2003 Bam, Iran, earthquake,” 2005.
  5. [5] M. Matsuoka, H. Miura, S. Midorikawa, and M. Estrada, “Extraction of urban information for seismic hazard and risk assessment in Lima, Peru using satellite imagery,” Journal of Disaster Research, Vol.8, No.2, pp. 328–345, 2013.
  6. [6] T. Hoshi, O. Murao, K. Yoshino, F. Yamazaki, and M. Estrada, “Post-disaster urban recovery monitoring in pisco after the 2007 peru earthquake using satellite image,” Journal of Disaster Research, Vol.9, No.6, pp. 1059–1068, 2014.
  7. [7] H. Gokon, S. Koshimura, and M. Matsuoka, “Object-based method for estimating Tsunami-induced damage using TerraSAR-X data,” Journal of Disaster Research, Vol.11, No.2, pp. 225–235, 2016.
  8. [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. [9] M. Matsuoka, S. Mito, S. Midorikawa, H. Miura, L. G. Quiroz, Y. Maruyama, and M. Estrada, “Development of Building Inventory Data and Earthquake Damage Estimation in Lima , Peru for Future Earthquakes,” Journal of Disaster Research, Vol.9, No.6, pp. 1032–1041, 2014.
  10. [10] 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.
  11. [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. [12] 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.
  13. [13] 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.
  14. [14] B. Guo, R. I. Damper, S. R. Gunn, and J. D. B. Nelson, “A fast separability-based feature-selection method for high-dimensional remotely sensed image classification,” Pattern Recognition, Vol.41, No.5, pp. 1670–1679, 2008.
  15. [15] L. Breiman, “Random forests,” Machine Learning, Vol.45, No.1, pp. 5–32, 2001.
  16. [16] S. A. Naghibi, H. R. Pourghasemi, and B. Dixon, “GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran,” Environmental Monitoring and Assessment, Vol.188, No.1, pp. 1–27, 2016.
  17. [17] O. Rahmati, H. R. Pourghasemi, and A. M. Melesse, “Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran,” Catena, Vol.137, pp. 360–372, 2016.
  18. [18] M. Belgiu and L. Drgu, “Random forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.114, pp. 24–31, 2016.
  19. [19] A. L. Mitchell, I. Tapley, A. K. Milne, M. L. Williams, Z. S. Zhou, E. Lehmann, P. Caccetta, K. Lowell, and A. Held, “C- and L-band SAR interoperability: Filling the gaps in continuous forest cover mapping in Tasmania,” Remote Sensing of Environment, Vol.155, pp. 58–68, 2014.
  20. [20] A. Solikhin, V. Pinel, J. Vandemeulebrouck, J. C. Thouret, and M. Hendrasto, “Mapping the 2010 Merapi pyroclastic deposits using dual-polarization Synthetic Aperture Radar (SAR) data,” Remote Sensing of Environment, Vol.158, pp. 180–192, 2015.
  21. [21] M. Shakya and C. K. Kawan, “Reconnaissance based damage survey of buildings in Kathmandu valley: An aftermath of 7.8Mw, 25 April 2015 Gorkha (Nepal) earthquake,” Engineering Failure Analysis, Vol.59, No.April, pp. 161–184, 2016.
  22. [22] E. Agency, “April 15, 2016 Japan Aerospace Exploration Agency,” tech. rep., 2016.
  23. [23] T. Esch, M. Thiel, A. Schenk, A. Roth, A. Müller, and S. Dech, “Delineation of Urban footprints from TerraSAR-X data by analyzing speckle characteristics and intensity information,” IEEE Transactions on Geoscience and Remote Sensing, Vol.48, No.2, pp. 905–916, 2010.
  24. [24] UNITAR, tech. rep., 2015, [accessed April 12, 2015]
  25. [25] C. N. Koyama, K. Schneider, and M. Sato, “Development of a Biomass Corrected Soil Moisture Retrieval Model for Dual-Polarization Alos-2 Data Based on Alos / Palsar and Pi-Sar-L2 Observations,” IGASS, No.April 2012, pp. 1316–1319, 2015.
  26. [26] J. Betbeder, S. Rapinel, S. Corgne, E. Pottier, and L. Hubert-Moy, “TerraSAR-X dual-pol time-series for mapping of wetland vegetation,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.107, pp. 90–98, 2015.
  27. [27] F. Nunziata, M. Migliaccio, S. Member, and C. E. Brown, “Re fl ection Symmetry for Polarimetric Observation of Man-Made Metallic Targets at Sea,” IEEE Journal of Oceanic Engineering, Vol.37, No.3, pp. 384–394, 2012.
  28. [28] D. South, D. Velotto, C. Bentes, B. Tings, and S. Lehner, “First Comparison of Sentinel-1 and TerraSAR-X Data in the Framework of Maritime Targets,” IEEE Journal of Oceanic Engineering, pp. 1–14, 2016.
  29. [29] ESA, 2015, [accessed May 12, 2016]
  30. [30] M. Tuceryan, M. Tuceryan, A. K. Jain, and A. K. Jain, “The Handbook of Pattern Recognition and Computer Vision (2nd Edition), Texture Analysis,” Pattern Recognition, pp. 207–248, 1998.
  31. [31] N. Li, R. Wang, Y. Deng, Y. Liu, B. Li, C. Wang, and T. Balz, “Unsupervised polarimetric synthetic aperture radar classification of large-scale landslides caused by Wenchuan earthquake in hue-saturation-intensity color space,” Journal of Applied Remote Sensing, Vol.8, No.1, p. 083595, 2014.
  32. [32] 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.
  33. [33] V. Lempitsky, M. Verhoek, J. A. Noble, and A. Blake, “Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.5528, pp. 447–456, 2009.
  34. [34] B. R. Smith, K. M. Ashton, A. Brodbelt, T. Dawson, M. D. Jenkinson, N. T. Hunt, D. S. Palmer, and M. J. Baker, “Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology,” The Analyst, pp. 3668–3678, 2016.
  35. [35] JiSoo Ham, Yangchi Chen, Melba M. Crawford, and J. Ghosh, “Investigation of the Random Forest Framework forbackslashrClassification of Hyperspectral Data,” IEEE Transactions on Geoscience and Remort Scensing, Vol.43, No.3, pp. 492–501, 2005.
  36. [36] B. Koch, “Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol.65, No.6, pp. 581–590, 2010.
  37. [37] L. Breiman, “Random forests,” Machine Learning, Vol.45, No.1, pp. 5–32, 2001.
  38. [38] L. Breiman, “Technical note: Some properties of splitting criteria,” Machine Learning, Vol.24, pp. 41–47, 1996.
  39. [39] Dan Steinberg, “Salford Systems,” 1983.
  40. [40] K. K. Nicodemus, J. D. Malley, C. Strobl, and A. Ziegler, “The behaviour of random forest permutation-based variable importance measures under predictor correlation,” BMC bioinformatics, Vol.11, p. 110, 2010.
  41. [41] D. Aldo, F. Dell, P. Gamba, and G. Lisini, “Earthquake damage assessment from post-event only radar satellite data,” October 2015.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024