@article {21444,
	title = {A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks},
	journal = {IEEE Geoscience and Remote Sensing Letters},
	volume = {15},
	number = {1},
	year = {2017},
	pages = {43 - 47},
	abstract = {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.},
	url = {http://ieeexplore.ieee.org/document/8126255/},
	author = {Yanbing Bai and Chang Gao and Sameer Singh and Magaly Koch and Bruno Adriano and Erick MAS and Shunichi KOSHIMURA}
}
