Lightweight remote sensing super-resolution with multi-scale graph attention network
发布时间：2025-04-01
点击次数：
- DOI码：
- 10.1016/j.patcog.2024.111178
- 所属单位：
- Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China
- 发表刊物：
- PATTERN RECOGNITION
- 项目来源：
- This work is supported by the National Natural Science Foundation of China (42090012, 62401410) , Sh
- 关键字：
- Remote sensing,Multi-scale network,Lightweight network,Super-resolution,Graph attention network
- 摘要：
- 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
- 合写作者：
- Lu, Tao,Huang, Xiao,Wang, Jiaming,Zhang, Zhizheng,Zuo, Xiaolong
- 第一作者：
- Wang, Yu
- 论文类型：
- 期刊论文
- 通讯作者：
- Shao, Zhenfeng
- 文献类型：
- Article
- 卷号：
- 160
- ISSN号：
- 0031-3203
- 是否译文：
- 否
- 发表时间：
- 2025-04-01