北京测绘2026，Vol.40Issue(9)：1241-1246,6.DOI:10.19580/j.cnki.1007-3000.2025080063
GF-1宽幅影像单图像超分辨率重建方法研究
Single-image super-resolution reconstruction for GF-1 wide-swath imagery
李珏 1沈鹏 2高露雄1
作者信息
- 1. 长江水利委员会水文局,湖北 武汉 430010
- 2. 95791部队,甘肃 酒泉 735018
- 折叠
摘要
Abstract
To further enhance the spatial resolution of Gaofen-1(GF-1)wide-swath imagery and expand its applicability in national condition monitoring—particularly hydrology and water resources—this study investigated the single-image super-resolution(SISR)problem for GF-1 wide-swath data.Six representative algorithms were selected from two broad categories—interpolation-based methods and adversarial neural-network approaches—and implemented.A test set was constructed that spanned resolutions from 16 m down to 2 m,to validate the super-resolution performance and effectiveness of the six algorithms under low,medium,and high scaling(up-sampling)factors.Assessment metrics included mean-square error(MSE)and visual-fidelity indices.The experiments show that,for the medium-and low-factor SISR requirements of GF-1 wide-swath imagery,all six methods can meet the task;deep-learning-based algorithms achieve the highest reconstruction quality,whereas interpolation-based techniques provide a clear advantage in computational efficiency.Consequently,when efficiency is a prior-ity,interpolation methods are recommended for medium-and low-factor super-resolution tasks;when sufficient computing resources are available,deep-learning algorithms should be adopted to achieve optimal results.For high-factor super-resolution,deep-learning methods remain the preferred choice,but attention should be paid to the artifacts they may introduce.关键词
高分一号(GF-1)/超分辨率/机器学习/深度学习Key words
Gaofen-1(GF-1)/super-resolution/machine learning/deep learning分类
天文与地球科学引用本文复制引用
李珏,沈鹏,高露雄..GF-1宽幅影像单图像超分辨率重建方法研究[J].北京测绘,2026,40(9):1241-1246,6.
