WaterDetectionNet: A New Deep Learning Method for Flood Mapping With SAR Image Convolutional Neural Network-Reference-Cited by-同舟云学术

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WaterDetectionNet: A New Deep Learning Method for Flood Mapping With SAR Image Convolutional Neural Network

Published:2024 Issue: Volume:17 Page:14471-14485
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Huang Binbin¹^ORCID,Li Peng¹^ORCID,Lu Hongyuan¹^ORCID,Yin Jiamin¹^ORCID,Li Zhenhong²^ORCID,Wang Houjie¹^ORCID

Affiliation:

1. Institute of Estuarine and Coastal Zone, College of Marine Geosciences, Key Laboratory of Submarine Geosciences and Prospecting Technology, Ministry of Education, Ocean University of China, Qingdao, China

2. College of Geological Engineering and Geomatics, Chang'an University, Xi'an, China

Funder

National Natural Science Foundation of China

National Training Program of Innovation and Entrepreneurship for Undergraduates

Ministry of Education

3rd Research Announcement on the Earth Observations Collaborative Research Agreement

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10632592.pdf?arnumber=10632592

Reference34 articles.

1. Satellite imaging reveals increased proportion of population exposed to floods

2. Global evidence of rapid urban growth in flood zones since 1985

3. Applying Remote Sensing to Support Flood Risk Assessment and Relief Agencies: A Global to Local Approach

4. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data

5. Strengthen China's flood control

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