Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning-Reference-Cited by-同舟云学术

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Enhancement of Urban Floodwater Mapping From Aerial Imagery With Dense Shadows via Semisupervised Learning

Published:2022 Issue: Volume:15 Page:9086-9101
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:

He Yongjun¹^ORCID,Wang Jinfei¹^ORCID,Zhang Ying²,Liao Chunhua³

Affiliation:

1. Department of Geography and Environment, The University of Western Ontario, London, ON, Canada

2. Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON, Canada

3. School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai, China

Funder

Canada Centre for Mapping and Earth Observation

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09924583.pdf?arnumber=9924583

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling;Remote Sensing of Environment;2024-02

2. An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples;ISPRS Journal of Photogrammetry and Remote Sensing;2024-01

3. Comprehensive study on the water body and environmental issues – A review;AIP Conference Proceedings;2024

4. MS4D-Net: Multitask-Based Semi-Supervised Semantic Segmentation Framework with Perturbed Dual Mean Teachers for Building Damage Assessment from High-Resolution Remote Sensing Imagery;Remote Sensing;2023-01-13

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