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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6165191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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