Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data
Abstract
:1. Introduction
2. Study Area and Data Set
3. Comparison of Pre- and Post-POLSAR Observables
3.1. Scattering Power Chnages
3.2. Matrix Dissimilarity Measure
3.3. Earthquake-Induced Scattering Mechanism Changes
4. Detection of Building-Damaged Areas from POLSAR Observables
4.1. Selection of Damage Indicator
4.2. Automatic Damage Detection
4.2.1. Binary Classification by Thresholding
4.2.2. Fuzzy-Based Contextual Classification
5. Discussion
5.1. Comparison with the Single-Polarization Damage Detector
5.2. Grid-Based Damage Index and Comparison with In-Situ Survey
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Matsuoka, M.; Yamazaki, F. Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes. Earthq. Spectra 2004, 2, 975–994. [Google Scholar] [CrossRef]
- Matsuoka, M.; Yamazaki, F. Building damage mapping of the 2003 Bam, Iran, earthquake using Envisat/ASAR intensity imagery. Earthq. Spectra 2005, 21, 285–294. [Google Scholar] [CrossRef]
- Matsuoka, M.; Yamazaki, F.; Ohkura, H. Damage mapping for the 2004 Niigata-ken Chuetsu earthquake using Radarsat images. In Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France, 11–13 April 2007. [Google Scholar]
- Liu, W.; Yamazaki, F. Extraction of collapsed buildings in the 2016 Kumamoto earthquake using multi-temporal PALSAR-2 data. J. Disaster Res. 2017, 12, 241–250. [Google Scholar] [CrossRef]
- Chini, M.; Pierdicca, N.; Emery, W.J. Exploiting SAR and VHR optical images to quantify damage caused by the 2003 Bam earthquake. IEEE Trans. Geosci. Remote Sens. 2009, 47, 145–152. [Google Scholar] [CrossRef]
- Dekker, R. High-resolution radar damage assessment after the earthquake in Haiti on 12 January 2010. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 960–970. [Google Scholar] [CrossRef]
- Park, S.-E.; Yamaguchi, Y.; Kim, D.J. Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR. Remote Sens. Environ. 2013, 132, 212–220. [Google Scholar] [CrossRef]
- Chen, S.-W.; Sato, M. Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images. IEEE Trans. Geosci. Remote Sens. 2013, 51, 1985–1997. [Google Scholar] [CrossRef]
- Chen, S.-W.; Wang, X.-S.; Sato, M. Urban damage level mapping based on scattering mechanism investigation using fully polarimetric SAR data for the 3.11 East Japan earthquake. IEEE Trans. Geosci. Remote Sens. 2016, 54, 6919–6929. [Google Scholar] [CrossRef]
- Kato, A.; Nakamura, K.; Hiyama, Y. The 2016 Kumamoto earthquake sequence. Proc. Jpn. Acad. Ser. B 2016, 92, 358–371. [Google Scholar] [CrossRef] [Green Version]
- 2016 Kumamoto Earthquake Verification Report of Correspondence by Mashiro-Machi. Available online: https://www.town.mashiki.lg.jp/bousai/kiji0032410/3_2410_1633_up_j7cvpcog.pdf (accessed on 30 September 2019). (In Japanese).
- Vasile, G.; Trouve, E.; Lee, J.-S.; Buzuloiu, V. Intensity-driven adaptive neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1609–1621. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.S.; Pottier, E. Polarimetric Radar Imaging—From Basics to Applications; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- Van Zyl, J.J.; Zebker, H.A.; Elachi, C. Imaging radar polarization signatures: Theory and observation. Radio Sci. 1987, 22, 529–543. [Google Scholar] [CrossRef]
- Conradsen, K.; Nielsen, A.A.; Schou, J.; Skriver, H. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 2003, 41, 4–19. [Google Scholar] [CrossRef] [Green Version]
- Kersten, P.R.; Lee, J.-S.; Ainsworth, T.L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering. IEEE Trans. Geosci. Remote Sens. 2005, 43, 519–527. [Google Scholar] [CrossRef]
- Anfinsen, S.N.; Jenssen, R.; Eltoft, T. Spectral clustering of polarimetric SAR data with Wishart-derived distance measures. In Proceedings of the POLInSAR 2007, Esrin, Italy, 22–26 January 2007. [Google Scholar]
- Frery, A.C.; Nascimento, A.D.C.; Cintra, R.J. Analytic expressions for stochastic distances between relaxed complex Wishart distributions. IEEE Trans. Geosci. Remote Sens. 2014, 52, 1213–1226. [Google Scholar] [CrossRef] [Green Version]
- Yang, J.; Peng, Y.; Lin, S. Similarity between two scattering matrices. Electron. Lett. 2001, 37, 193–194. [Google Scholar] [CrossRef]
- An, W.; Zhang, W.; Yang, J.; Hong, W. Similarity between two targets and its application to polarimetric target detection for sea area. In Proceedings of the 24th PIERS, Cambridge, MA, USA, 1–6 July 2008; pp. 515–520. [Google Scholar]
- Lee, J.-S.; Schuler, D.L.; Ainsworth, T.L.; Krogager, E.; Kasilingam, D.; Boerner, W.-M. On the estimation of radar polarization orientation shifts induced by terrain slopes. IEEE Trans. Geosci. Remote Sens. 2002, 40, 30–41. [Google Scholar]
- Yamaguchi, Y.; Sato, A.; Boerner, W.M.; Sato, R.; Yamada, H. Four-component scattering power decomposition with rotation of coherency matrix. IEEE Trans. Geosci. Remote Sens. 2011, 49, 2251–2258. [Google Scholar] [CrossRef]
- Cloude, S.R. Polarisation: Applications in Remote Sensing; Oxford University Press: New York, NY, USA, 2010. [Google Scholar]
- Cloude, S.R.; Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 1996, 34, 498–518. [Google Scholar] [CrossRef]
- Otsu, N. A threshold selection method from grey level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef] [Green Version]
- Kittler, J.; Illingworth, J. Minimum error thresholding. Pattern Recognit. 1986, 19, 41–47. [Google Scholar] [CrossRef]
- Bruzzone, L.; Prieto, D.F. Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 2000, 38, 1171–1182. [Google Scholar] [CrossRef] [Green Version]
- Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20, 37–46. [Google Scholar] [CrossRef]
- Foulkes, S.B.; Dooth, D.M. Ship detection in ERS and Radarsat imagery using a self-organising Kohonen neural network. In Proceedings of the Nova Scotia Conference on Ship Detection in Coastal Waters, Digby, NS, Canada, 31 May–1 June 2000. [Google Scholar]
- Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef] [Green Version]
- Solaiman, B.; Pierce, L.; Ulaby, F. Multisensor data fusion using fuzzy concepts: Application to land-cover classification using ERS-1/JERS-1 SAR composites. IEEE Trans. Geosci. Remote Sens. 1999, 37, 1316–1326. [Google Scholar] [CrossRef] [Green Version]
- Fauvel, M.; Chanussot, J.; Benediktsson, J.A. Decision fusion for the classification of urban remote sensing images. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2828–2838. [Google Scholar] [CrossRef]
- National Institute for Land and Infrastructure Management (NILIM), Quick Report of the Field Survey on the Building Damage by the 2016 Kumamoto Earthquake, Technical Note No.929. Available online: http://www.nilim.go.jp/lab/bcg/siryou/tnn/tnn0929.htm (accessed on 30 September 2019). (In Japanese).
- Okada, S.; Takai, N. Classifications of structural types and damage patterns of buildings for earthquake field investigation. In Proceedings of the 12th World Conference on Earthquake Engineering, Auckland, New Zealand, 30 January–4 February 2000. [Google Scholar]
- Plank, S. Rapid Damage Assessment by Means of Multi-Temporal SAR–A Comprehensive Review and Outlook to Sentinel-1. Remote Sens. 2014, 6, 4870–4906. [Google Scholar] [CrossRef] [Green Version]
Accuracy Metric | ||||||
---|---|---|---|---|---|---|
Otsu | KI | EM | Otsu | KI | EM | |
Detection rate | 94.63% | 6.38% | 21.38% | 94.12% | 10.52% | 37.05% |
False-alarm rate | 34.31% | 0.08% | 0.45% | 26.83% | 0.14% | 1.16% |
Kappa | 13.04% | 11.23% | 31.11% | 17.59% | 17.68% | 43.65% |
FOM | 10.95% | 6.27% | 19.42% | 13.49% | 10.21% | 29.46% |
Accuracy Metric | Fuzzy Membership Fusion () |
---|---|
Detection rate | 90.95% |
False-alarm rate | 1.27% |
FOM | 81.34% |
Kappa | 69.72% |
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Park, S.-E.; Jung, Y.T. Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data. Remote Sens. 2020, 12, 137. https://doi.org/10.3390/rs12010137
Park S-E, Jung YT. Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data. Remote Sensing. 2020; 12(1):137. https://doi.org/10.3390/rs12010137
Chicago/Turabian StylePark, Sang-Eun, and Yoon Taek Jung. 2020. "Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data" Remote Sensing 12, no. 1: 137. https://doi.org/10.3390/rs12010137