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

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 requireme...

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Detalles Bibliográficos
Autores principales: Kang, Wenchao, Xiang, Yuming, Wang, Feng, Wan, Ling, You, Hongjian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165191/
https://www.ncbi.nlm.nih.gov/pubmed/30200546
http://dx.doi.org/10.3390/s18092915
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author Kang, Wenchao
Xiang, Yuming
Wang, Feng
Wan, Ling
You, Hongjian
author_facet Kang, Wenchao
Xiang, Yuming
Wang, Feng
Wan, Ling
You, Hongjian
author_sort Kang, Wenchao
collection PubMed
description 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.
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spelling pubmed-61651912018-10-10 Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks Kang, Wenchao Xiang, Yuming Wang, Feng Wan, Ling You, Hongjian Sensors (Basel) Article 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. MDPI 2018-09-02 /pmc/articles/PMC6165191/ /pubmed/30200546 http://dx.doi.org/10.3390/s18092915 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kang, Wenchao
Xiang, Yuming
Wang, Feng
Wan, Ling
You, Hongjian
Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title_full Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title_fullStr Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title_full_unstemmed Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title_short Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks
title_sort flood detection in gaofen-3 sar images via fully convolutional networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165191/
https://www.ncbi.nlm.nih.gov/pubmed/30200546
http://dx.doi.org/10.3390/s18092915
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AT wanling flooddetectioningaofen3sarimagesviafullyconvolutionalnetworks
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