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15827188114Status:
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测绘遥感信息工程全国重点实验室Date of Birth:
1976-03-21Alma Mater:
武汉大学Status:
EmployedSchool/Department:
测绘遥感信息工程全国重点实验室Date of Employment:
2004-07-01Administrative Position:
副主任Business Address:
武汉大学星湖实验大楼713Contact Information:
15827188114
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Paper Publications
Current position: Home > Scientific Research > Paper Publications
Lightweight remote sensing super-resolution with multi-scale graph attention network
- Time:2025-04-01
- Hits:
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DOI number:
10.1016/j.patcog.2024.111178 -
Affiliation of Author(s):
Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China -
Journal:
PATTERN RECOGNITION -
Funded by:
This work is supported by the National Natural Science Foundation of China (42090012, 62401410) , Sh -
Key Words:
Remote sensing,Multi-scale network,Lightweight network,Super-resolution,Graph attention network -
Abstract:
Remote Sensing Super-Resolution (RS-SR) constitutes a pivotal component in the domain of remote sensing image analysis, aimed at enhancing the spatial resolution of low-resolution imagery. Recent advancements have seen deep learning techniques achieving substantial progress in the RS-SR field. Notably, Graph Neural Networks (GNNs) have emerged as a potent mechanism for processing remote sensing images, adept at elucidating the intricate inter-pixel relationships within images. Nevertheless, a prevalent limitation among existing GNN-based methodologies is their disregard for the high computatio -
Co-author:
Xiao, Xiaolong,Zuo, Zhizheng,Zhang, Jiaming,Wang,Huang, Tao,Lu -
Indexed by:
Journal paper -
Correspondence Author:
Shao, Zhenfeng -
Document Type:
Article -
Volume:
160 -
ISSN No.:
0031-3203 -
Translation or Not:
no -
Date of Publication:
2025-04-01