Edge-SAR-Assisted Multimodal Fusion for Enhanced Cloud Removal-Reference-Cited by-同舟云学术

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Edge-SAR-Assisted Multimodal Fusion for Enhanced Cloud Removal

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Wen Zhenyu¹^ORCID,Suo Jiahui²,Su Jie³^ORCID,Li Bingning⁴,Zhou Yejian⁵^ORCID

Affiliation:

1. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

2. Zhejiang University of Technology, Hangzhou, China

3. School of Computing, Newcastle University, Newcastle Upon Tyne, U.K

4. Xi’an Satellite Control Center, Xi’an, China

5. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

Funder

National Natural Sciences Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10225590.pdf?arnumber=10225590

Reference24 articles.

1. Edge-Preserving Convolutional Generative Adversarial Networks for SAR-to-Optical Image Translation

2. Cloud removal for remote sensing imagery via spatial attention generative adversarial network;pan;arXiv 2009 13015,2020

3. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

4. Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

5. SAR-SIFT: A SIFT-Like Algorithm for SAR Images

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1. Breaking through clouds: A hierarchical fusion network empowered by dual-domain cross-modality interactive attention for cloud-free image reconstruction;Information Fusion;2025-01

2. LGE-CR: Integrating Nonlocal Gated Attention and Two-Flow Residual Dense Learning for Cloud Removal;IEEE Geoscience and Remote Sensing Letters;2024

3. UAV and SAT Images to Monitor Climate Conditions and Crop Yielding – a Survey;Lecture Notes in Networks and Systems;2024

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