Efficient Remote-Sensing Segmentation With Generative Adversarial Transformer-Reference-Cited by-同舟云学术

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Efficient Remote-Sensing Segmentation With Generative Adversarial Transformer

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Qiu Luyi¹^ORCID,Yu Dayu²^ORCID,Zhang Xiaofeng³^ORCID,Zhang Chenxiao²

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

China National Postdoctoral Program for Innovative Talents

Natural Science Foundation of Hubei Province

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10343157.pdf?arnumber=10343157

Reference21 articles.

1. MsanlfNet: Semantic Segmentation Network With Multiscale Attention and Nonlocal Filters for High-Resolution Remote Sensing Images

2. Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation

3. A Local–Global Framework for Semantic Segmentation of Multisource Remote Sensing Images

4. Learning longitudinal classification-regression model for infant hippocampus segmentation

5. High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

Cited by 1 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. ER-Swin: Feature Enhancement and Refinement Network Based on Swin Transformer for Semantic Segmentation of Remote Sensing Images;IEEE Geoscience and Remote Sensing Letters;2024

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