A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter,...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-01, Vol.15 (1), p.43-47
Main Authors: Yanbing Bai, Chang Gao, Singh, Sameer, Koch, Magaly, Adriano, Bruno, Mas, Erick, Koshimura, Shunichi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Building damage
Damage detection
Deep neural networks
Disaster relief
Earthquake damage
Earthquakes
framework
Frameworks
Image processing
Image recognition
Imagery
Machine learning
Mapping
Neural networks
post-event TerraSAR-X imagery
Radar
rapid
Recognition
regional tsunami damage recognition
Regions
SAR (radar)
Seismic activity
Storm damage
Synthetic aperture radar
Tiles
Training
Tsunami
Tsunami damage
Tsunamis
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Summary:Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2772349