Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Full Polarimetric SAR Decomposition Analysis of Landslide-affected Areas in Mocoa, Colombia

  • Jeon, Hyeong-Joo (Dept. of Civil and Environmental Engineering, Seoul National University) ;
  • Kim, Yong-Il (Dept. of Civil and Environmental Engineering, Seoul National University)
  • Received : 2017.09.26
  • Accepted : 2017.10.27
  • Published : 2017.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

SAR (Synthetic Aperture Radar) is an effective tool for monitoring areas damaged by disasters. Full PolSAR (Polarimetric SAR) enhances SAR's capabilities by providing specific scattering mechanisms. Thus, full PolSAR data have been widely used to analyze the situation when disasters occur. To interpret full PolSAR data, model-based decomposition methods are frequently used due to its easy physical interpretation of PolSAR data and computational efficiency. However, these methods present problems. One of the key problems is the overestimation of the volume scattering component. To minimize the volume scattering component, the OA (Orientation Angle) compensation method is widely utilized. This paper shows that the effect of the OA compensation was analyzed over landslide affected areas. In this paper, the OA compensation is applied by using the OA estimated from the maximum relative Hellinger distance. We conducted an experiment using two full polarimetric ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar)-2 data collected over Mocoa, Colombia which was seriously damaged by the 2017 Mocoa landslide. After OA compensation, the experimental results showed volume scattering power decreased, while the double-bounce and surface scattering power increased. Particularly, significant changes were noted in urban areas. In addition, after OA compensation, the separability of the double-bounce and surface scattering components are improved over the damaged building areas. Furthermore, changes in the OA can discriminate visually between the damaged building areas and undamaged areas. In conclusion, we demonstrated that the effect of OA compensation improved the influence of the double-bounce and surface scattering components, and OA changes can be useful for detecting damaged building areas.

Keywords

References

  1. Arii, M., van Zyl, J. J., and Kim, Y. (2011), Adaptive modelbased decomposition of polarimetric SAR covariance matrices, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 3, pp. 1104-1113 https://doi.org/10.1109/TGRS.2010.2076285
  2. Bhattacharya, A., Muhuri, A., De, S., Manickam, S., and Frery, A. C. (2015), Modifying the Yamaguchi four-component decomposition scattering powers using a stochastic distance, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 7, pp. 3497-3506. https://doi.org/10.1109/JSTARS.2015.2420683
  3. Chen, S. W. and Sato, M. (2013), Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 4, pp. 1985-1997. https://doi.org/10.1109/TGRS.2012.2210050
  4. Chen, S. W., Wang, X. S., Li, Y. Z., and Sato, M. (2014a), Adaptive model-based polarimetric decomposition using PolInSAR coherence, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, NO. 3, pp. 1705-1718. https://doi.org/10.1109/TGRS.2013.2253780
  5. Chen, S. W., Wang, X. S., Xiao, S. P., and Sato, M. (2014b), General polarimetric model-based decomposition for coherency matrix, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 3, pp. 1843-1855. https://doi.org/10.1109/TGRS.2013.2255615
  6. Chen, S. W., Li, Y. Z., Wang, X. S., Xiao, S. P., and Sato, M. (2014c), Modeling and interpretation of scattering mechanisms in polarimetric synthetic aperture radar: Advances and perspectives, IEEE Signal Processing Magazine, Vol. 31, No. 4, pp. 79-89. https://doi.org/10.1109/MSP.2014.2312099
  7. Freeman, A. and Durden, S. L. (1998), A three-component scattering model for polarimetric SAR data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 3, pp. 963-973. https://doi.org/10.1109/36.673687
  8. Hoyois, P., Scheuren, J. M., Below, R., and Guha-Sapir, D. (2007), Annual Disaster Statistical Review: Numbers and Trends 2006, The number and trends, Centre for Research on the Epidemiology of Disasters, 48p.
  9. Kullback, S. and Leibler, R. A. (1951), On information and sufficiency, The Annals of Mathematical Statistics, Vo. 22, No. 1, pp. 79-86. https://doi.org/10.1214/aoms/1177729694
  10. Lee, J. S. and Ainsworth, T. L. (2011), The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 1, pp. 53-64. https://doi.org/10.1109/TGRS.2010.2048333
  11. Lee, J. S. and Pottier, E. (2009), Polarimetric Radar Imaging: from Basics to Applications, CRC press, Boca Raton.
  12. Li, N., Wang, R., Deng, Y., Liu, Y., Wang, C., Balz, T., and Li, B. (2014), Polarimetric response of landslides at X-band following the Wenchuan earthquake, IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 10, pp. 1722-1726. https://doi.org/10.1109/LGRS.2014.2306820
  13. Luo, S., Tong, L., Chen, Y., and Tan, L. (2016), Landslides identification based on polarimetric decomposition techniques using Radarsat-2 polarimetric images, International Journal of Remote Sensing, Vol. 37, No. 12, pp. 2831-2843.
  14. Moriyama, T., Uratsuka, S., Umehara, T., Satake, M., Nadai, A., Maeno, H., and Yamaguchi, Y. (2004), A study on extraction of urban areas from polarimetric synthetic aperture radar image, In Geoscience and Remote Sensing Symposium 2004, IEEE, 20-24 Septemeber, Anchorage, USA, Vol. 1, pp. 703-706.
  15. Park, S. E., Yamaguchi, Y., and Kim, D. J. (2013), Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR, Remote Sensing of Environment, Vol. 132, pp. 212-220. https://doi.org/10.1016/j.rse.2013.01.018
  16. Sato, M., Chen, S. W., and Satake, M. (2012), 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. https://doi.org/10.1109/JPROC.2012.2200649
  17. Shibayama, T., Yamaguchi, Y., and Yamada, H. (2015), Polarimetric scattering properties of landslides in forested areas and the dependence on the local incidence angle, Remote Sensing, Vol. 7, No. 11, pp. 15424-15442. https://doi.org/10.3390/rs71115424
  18. UNITAR. (2017), Potentially affected zones by the mudflow in mocoa, colombia, United Nations Institute for Training and Research, Switzerland, http://unosat-maps.web.cern.ch/unosat-maps/CO/LS20170403COL/UNOSAT_A3_Mocoa_Colombia_MudFlow_10April2017.pdf (last date accessed: 16 Oct 2017).
  19. Van Zyl, J. J., Arii, M., and Kim, Y. (2011), Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 9, pp. 3452-3459. https://doi.org/10.1109/TGRS.2011.2128325
  20. Watanabe, M., Yonezawa, C., Iisaka, J., and Sato, M. (2012), ALOS/PALSAR full polarimetric observations of the Iwate-Miyagi Nairiku earthquake of 2008, International Journal of Remote Sensing, Vol. 33, No. 4, pp. 1234-1245. https://doi.org/10.1080/01431161.2011.554453
  21. Yamaguchi, Y., Moriyama, T., Ishido, M., and Yamada, H. (2005), Four-component scattering model for polarimetric SAR image decomposition, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 8, pp. 1699-1706. https://doi.org/10.1109/TGRS.2005.852084
  22. Yamaguchi, Y. (2012), Disaster monitoring by fully polarimetric SAR data acquired with ALOS-PALSAR, Proceedings of the IEEE, Vol. 100, No. 10, pp. 2851-2860. https://doi.org/10.1109/JPROC.2012.2195469
  23. Yonezawa, C., Watanabe, M., and Saito, G. (2012), Polarimetric decomposition analysis of ALOS PALSAR observation data before and after a landslide event, Remote Sensing, Vol. 4, No. 8, pp. 2314-2328. https://doi.org/10.3390/rs4082314