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Development of Landslide Detection Algorithm Using Fully Polarimetric ALOS-2 SAR Data

Fully-Polarimetric ALOS-2 자료를 이용한 산사태 탐지 알고리즘 개발

  • Kim, Minhwa (Department of Energy and Mineral Resources Engineering, Sejong University) ;
  • Cho, KeunHoo (Department of Geoinformation Engineering, Sejong University) ;
  • Park, Sang-Eun (Department of Energy and Mineral Resources Engineering, Sejong University) ;
  • Cho, Jae-Hyoung (Radar R&D Center, Hanwha System) ;
  • Moon, Hyoi (Radar R&D Center, Hanwha System) ;
  • Han, Seung-hoon (Radar R&D Center, Hanwha System)
  • 김민화 (세종대학교 에너지자원공학과) ;
  • 조근후 (세종대학교 지구정보공학과) ;
  • 박상은 (세종대학교 에너지자원공학과) ;
  • 조재형 (한화시스템 레이더 R&D 센터) ;
  • 문효이 (한화시스템 레이더 R&D 센터) ;
  • 한승훈 (한화시스템 레이더 R&D 센터)
  • Received : 2019.07.29
  • Accepted : 2019.08.26
  • Published : 2019.08.28

Abstract

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

Keywords

landslide;polarimetric SAR;microwave scattering;speckle filtering;orthorectification

Acknowledgement

Supported by : 한화시스템

References

  1. Chigira, M., Tsou, C. Y., Matsushi, Y., Hiraishi, N. and Matsuzawa, M. (2013) Topographic precursors and geological structures of deep-seated catastrophic landslides caused by Typhoon Talas. Geomorphology, v.201, p.479-493. https://doi.org/10.1016/j.geomorph.2013.07.020
  2. Cloude, S. R. and Pottier, E. (1996) A review of target decomposition theorems in radar polarimetry. IEEE transactions on geoscience and remote sensing, v.34, p.498-518. https://doi.org/10.1109/36.485127
  3. Czuchlewski, K. R., Weissel, J. K. and Kim, Y. (2003) Polarimetric synthetic aperture radar study of the Tsaoling landslide generated by the 1999 Chi-Chi earthquake, Taiwan. Journal of Geophysical Research: Earth Surface, v.108.
  4. Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M. and Chang, K.-T. (2012) Landslide inventory maps: New tools for an old problem. Earth Science Reviews, v.112, p.42-66. https://doi.org/10.1016/j.earscirev.2012.02.001
  5. Kittler, J. and Illingworth, J. (1986) Minimum error thresholding, Pattern Recognition, v.19, p.41-47. https://doi.org/10.1016/0031-3203(86)90030-0
  6. Lee, J. S. (1981) Refined filtering of image noise using local statistics. Computer graphics and image processing, v.15, p.380-389. https://doi.org/10.1016/S0146-664X(81)80018-4
  7. Lee, J. S. (1983) Digital image smoothing and the sigma filter. Computer vision, graphics, and image processing, v.24, p.255-269. https://doi.org/10.1016/0734-189X(83)90047-6
  8. Lee, J. S. and Pottier, E. (2009) Polarimetric radar imaging: from basics to applications. CRC press, Boca Raton, FL, USA.
  9. Mondini, A. (2017) Measures of Spatial Autocorrelation Changes in Multitemporal SAR images for event landslides detection. Remote Sensing, v.9, 554. https://doi.org/10.3390/rs9060554
  10. Park, S.-E., Moon, W.M., Pottier, E. (2012) Assessment of scattering mechanism of polarimetric SAR signal from mountainous forest areas. IEEE Transactions on Geoscience and Remote Sensing, v.50(11), p.4711-4719. https://doi.org/10.1109/TGRS.2012.2194153
  11. Plank, S., Twele, A. and Martinis, S. (2016) Landslide mapping in vegetated areas using change detection based on optical and polarimetric SAR data. Remote Sensing, v.8, 307. https://doi.org/10.3390/rs8040307
  12. Schaefer, J. T. (1990) The critical success index as an indicator of warning skill. Weather and Forecasting, v.5, p.570-575. https://doi.org/10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2
  13. 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, v.7, p.15424-15442. https://doi.org/10.3390/rs71115424
  14. Small, D. and Schubert, A. (2008) Guide to ASAR Geocoding, RSL-ASAR-GC-AD, European Space Agency (ESA), Paris, France.
  15. Vasile, G., Trouve, E., Lee, J. S. and Buzuloiu, V. (2006) Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Transactions on Geoscience and Remote Sensing, v.44, p.1609-1621. https://doi.org/10.1109/TGRS.2005.864142
  16. Yamada, M., Matsushi, Y., Chigira, M. and Mori, J. (2012). Seismic recordings of landslides caused by Typhoon Talas (2011), Japan. Geophysical Research Letters, v.39.