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Remote Sens. 2019, 11(8), 897; https://doi.org/10.3390/rs11080897

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

1
Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou 730000, China
2
Key Laboratory of Loess Earthquake Engineering of China Earthquake Administration, Lanzhou 730000, China
3
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Received: 1 March 2019 / Revised: 25 March 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study. View Full-Text
Keywords: Earthquake; buildings; damage assessment; texture features; feature fusion; PolSAR; collapsed buildings Earthquake; buildings; damage assessment; texture features; feature fusion; PolSAR; collapsed buildings
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhai, W.; Huang, C.; Pei, W. Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image. Remote Sens. 2019, 11, 897.

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