Browse by author
Lookup NU author(s): Dr Lixia Gong
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Gong LX, Wang C, Wu F, Zhang JF, Zhang H, Li Q
Publication type: Article
Publication status: Published
Journal: Remoting Sensing
Year: 2016
Volume: 8
Issue: 11
Online publication date: 27/10/2016
Acceptance date: 24/10/2016
Date deposited: 01/02/2017
ISSN (electronic): 2072-4292
Publisher: MDPI AG
URL: http://dx.doi.org/10.3390/rs8110887
DOI: 10.3390/rs8110887
Altmetrics provided by Altmetric
