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Sensors 2018, 18(9), 2915;

Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

1,2,3,* , 2,3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China
Author to whom correspondence should be addressed.
Received: 26 June 2018 / Revised: 23 August 2018 / Accepted: 31 August 2018 / Published: 2 September 2018
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in this paper. Considering the requirement of flood detection, we fine-tune the model to get higher accuracy results with shorter training time and fewer training samples. Compared with state-of-the-art methods, our proposed algorithm not only gives robust and accurate detection results but also significantly reduces the detection time. View Full-Text
Keywords: SAR; flood detection; FCN; GF-3 satellite SAR; flood detection; FCN; GF-3 satellite

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Kang, W.; Xiang, Y.; Wang, F.; Wan, L.; You, H. Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks. Sensors 2018, 18, 2915.

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