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Sensors 2018, 18(9), 2915; https://doi.org/10.3390/s18092915

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

1,2,3
,
1,2,3,* , 2,3
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and
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1
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
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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Abstract

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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