Building damage mapping based on Touzi decomposition using quad-polarimetric ALOS PALSAR data

doi:10.1007/s11707-019-0779-3

Front. Earth Sci.

2020, Vol. 14

Issue (2) : 401-412 https://doi.org/10.1007/s11707-019-0779-3

RESEARCH ARTICLE

Building damage mapping based on Touzi decomposition using quad-polarimetric ALOS PALSAR data

Shan LIU^1,^2,³, Fengli ZHANG^1,^2,³(

), Shiying WEI⁴, Qingbo LIU^1,^2,³, Na LIU^1,^2,³, Yun SHAO^1,^2,³, Steven J. BURIAN⁵

¹. Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100094, China
². University of Chinese Academy of Sciences, Beijing 100049, China
³. Laboratory of Target Microwave Properties, Deqing Academy of Satellite Applications, Huzhou 313200, China
⁴. Beijing University of Civil Engineering and Architecture, Beijing 100044, China
⁵. Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA

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Abstract

Building damage assessment is of great significance to disaster monitoring. Polarimetric Synthetic Aperture Radar (SAR) can record the polarization scattering measurement matrix of ground objects and obtain more abundant ground object information, meaning that they can be used for assessing damage to buildings. In this paper, a new approach is proposed to assess building damage using the Touzi incoherent decomposition and SAR-based characteristics of buildings before and after damage. The March 11th, 2011 earthquake that struck the coast of north-east Japan serves as the demonstration of the technique using quad-polarimetric ALOS PALSAR data acquired before and after the disaster. The analysis shows that after the buildings are damaged, there is a clear decrease in the $α s 1$ (the dominant scattering-type magnitude) components and the degree of this reduction corresponds to the degree of building damage. This means that the $α s 1$ components obtained by Touzi decomposition can effectively reflect the degree of building damage. On this basis, a model based on Touzi decomposition was established to evaluate the degree of damage to buildings, and the accuracy of the model was validated using high-resolution optical data acquired before and after the earthquake. The experimental results show that Touzi decomposition can be effectively used for damage assessment mapping in built-up areas.

Keywords Touzi decomposition quad-polarization SAR buildings damage disaster management

Corresponding Author(s): Fengli ZHANG

Online First Date: 15 January 2020 Issue Date: 21 July 2020

Cite this article:

Shan LIU,Fengli ZHANG,Shiying WEI, et al. Building damage mapping based on Touzi decomposition using quad-polarimetric ALOS PALSAR data[J]. Front. Earth Sci., 2020, 14(2): 401-412.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0779-3
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I2/401

Tab.1 Detailed parameters of the three scenes of ALOS PALSAR data

Fig.1 HH polarimetric ALOS PALSAR images pre- and post-disaster. The red boxes mark the extent of experimental area for building damage mapping.

Fig.2 GeoEye-1 images with a resolution of 0.5 m before (a) and after (b) the disaster. The nine white circles numbered 1−9 represent four levels of damage, and within them there are 12 red sample areas A−L with damage degree calculated accurately. The yellow circles and blue boxes a−d show the samples used for method analyzing and validation.

Fig.3 Scattering mechanisms of buildings pre- and post-disaster.

Fig.4 Average values of

α s 1

from Touzi decomposition pre- and post-disaster for the 9 sample regions.

Fig.5 Flowchart of the proposed method.

Fig.6

α s 1

components extracted from the three ALOS PALSAR images.

Fig.7 Probability distributions of

α s 1

values for buildings with different damage levels extracted from ALOS PALSAR images of the three dates.

Fig.8 The distribution curve of

α s 1

values after sorting for buildings with different damage levels extracted from ALOS PALSAR images of the three dates.

Fig.9 Damage indexes for the 9 regions obtained using three ALOS PALSAR images.

Fig.10 (a) Extracted urban areas superimposed on the ALOS PALSAR image from November 21, 2010 (R: HH, G: HV, B: VV); (b) Damage index map of built-up areas superimposed on the HH polarimetric ALOS PALSAR image from November 21, 2010.

Fig.11 The GeoEye-1 images for the 12 sample areas (A−L) acquired on June 5, 2010 and March 19, 2011.

Fig.12 Relationship between the damage degree

D D ground_truth

and the damage index

Ratio α s 1

Fig.13 The damage index of the five sample areas that did not experience building collapse.

Fig.14 The calculated damage degree map and detailed map for verification sample areas.

Fig.15 The GeoEye-1 images for the four validation sample areas (a–d) acquired on June 5, 2010 and March 19, 2011.

Tab.2 Error evaluation of damage degree mapping

1	A Bhattacharya, R Touzi (2012). Polarimetric SAR urban classification using the Touzi target scattering decomposition. Can J Rem Sens, 37(4): 323–332 https://doi.org/10.5589/m11-042
2	L Z Chen, X H Shen, Q J Tian (2003). SAR technique and its application to geologic and seismic research. Earthquake, 23: 30–36 (in Chinese)
3	Q H Chen, Y L Nie, L L Li, X G Liu (2017). Buildings damage assessment using texture features of polarization decomposition components. J Remot Sens, 21: 955–965
4	S W Chen, M Sato (2013). Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images. IEEE Trans Geosci Remote Sens, 51(4): 1985–1997 https://doi.org/10.1109/TGRS.2012.2210050
5	S W Chen, X S Wang, M Sato (2016). 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, 54(12): 6919–6929 https://doi.org/10.1109/TGRS.2016.2588325
6	S R Cloude (1986). Group theory and polarisation algebra. Optik (Stuttg), 75: 26–36
7	H D Guo, X W Li, L Zhang (2009). Study of detecting method with advanced airborne and spaceborne synthetic aperture radar data for collapsed urban buildings from the Wenchuan earthquake. J Appl Remote Sens, 3(1): 131–136 https://doi.org/10.1117/1.3153902
8	H M Hao, Y H Zhang, H Y Shi (2012). Application of test statistic method in fully polarimtric SAR change detection. Yaogan Xuebao, 16: 520–532
9	K Li, Y Shao, R Touzi, B Brisco, F L Zhang (2011). Rice monitoring using touzi decomposition based on polarimetric SAR data in southwestern China. In: Proceedings of Progress in Electromagnetics Research Symposium. Suzhou: 827–830
10	X W Li, H D Guo, L Zhang, X Chen, L Liang (2012). A new approach to collapsed building extraction using RADARSAT-2 polarimetric SAR imagery. IEEE Geosci Remote Sens Lett, 9(4): 677–681 https://doi.org/10.1109/LGRS.2011.2178392
11	M Matsuoka, F Yamazaki (2000). Characteristics of satellite SAR images in the areas damaged by earthquakes. In: Blanchard A, Goodenough D, eds. Geoscience and Remote Sensing. Honolulu: IEEE, 2693–2696
12	S E Park, Y Yamaguchi, G Singh, H Kobayashi (2012). Polarimetric SAR remote sensing of earthquake/tsunami disaster. In: Moreira A, Desnos L, eds. Geoscience and Remote Sensing. Munich: IEEE, 1170–1173
13	J C Shen, X Xu, H Dong, R Gui, C Song (2015). Collapsed building extraction from single full polarimetric SAR image after earthquake. Sci Technology Eng, 15: 86–91 (in Chinsese)
14	R Touzi (2006). Target scattering decomposition in terms of roll-invariant target parameters. IEEE Trans Geosci Remote Sens, 45(1): 73–84 https://doi.org/10.1109/TGRS.2006.886176
15	X Q Wang, D J Jin, A Dou (2011). Study on the fractal analysis of SAR images and its application to the extraction of seismic damage of 2010 Yushu, China Ms=7.1 earthquake. In: Sato M, eds. Geoscience and Remote Sensing. Vancouver: IEEE, 1981–1984
16	T Yang, H L Gong, X J Li, W J Zhao (2010). Application of SAR to remote sensing of geological disasters. J Nat Disaster, 19(5): 42–48
17	C Yonezawa, S Takeuchi (1999). Detection of urban damage using interferometric SAR decorrelation. In: Alpers W, eds. Geoscience and Remote Sensing. Hamburg: IEEE, 925–927
18	W Zhai, C L Huang (2016). Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake. Earth Planets Space, 68(1): 1–12 https://doi.org/10.1186/s40623-016-0469-2
19	W Zhai, C L Huang, W S Pei (2018). Two new polarimetric feature parameters for the recognition of the different kinds of buildings in earthquake-stricken areas based on entropy and eigenvalues of PolSAR decomposition. Remote Sensing, 10.10: 1613
20	W Zhai, H F Shen, C L Huang, W S Pei (2016). Building earthquake damage information extraction from a single post-earthquake PolSAR image. Remote Sens, 8(3): 171 https://doi.org/10.3390/rs8030171
21	H Z Zhang, Q Wang, Q M Zeng, J Jiao (2015). A novel approach to building collapse detection from post-seismic polarimetric SAR imagery by using optimization of polarimetric contrast enhancement. In: Pascazio V, B. Serpico S, eds. Geoscience and Remote Sensing. Milan: IEEE, 3270–3273

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