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Remote Sens. 2016, 8(11), 887; doi:10.3390/rs8110887

Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery

1
Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China
2
School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
3
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editors: Roberto Tomas, Zhenhong Li, Zhong Lu, Richard Gloaguen and Prasad S. Thenkabail
Received: 30 June 2016 / Revised: 9 October 2016 / Accepted: 24 October 2016 / Published: 27 October 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
View Full-Text   |   Download PDF [4860 KB, uploaded 27 October 2016]   |  

Abstract

Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China. View Full-Text
Keywords: earthquake; damage assessment; building; Synthetic Aperture Radar; TerraSAR-X; high resolution earthquake; damage assessment; building; Synthetic Aperture Radar; TerraSAR-X; high resolution
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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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MDPI and ACS Style

Gong, L.; Wang, C.; Wu, F.; Zhang, J.; Zhang, H.; Li, Q. Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery. Remote Sens. 2016, 8, 887.

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