Land-cover prior diffusion probabilistic model for remote sensing image super resolution
发布时间：2026-04-01
点击次数：
- DOI码：
- 10.1016/j.patcog.2025.112577
- 所属单位：
- Wuhan Univ, State Key Lab Informat Engn Surveying Mapping &, Wuhan 430079, Peoples R China
- 发表刊物：
- PATTERN RECOGNITION
- 项目来源：
- This work is supported by National Key Research and Development Program of China (2023YFE0110400, 20
- 关键字：
- Remote sensing image super-resolution,Diffusion probabilistic model,Land cover prior,Transformer
- 摘要：
- Remote sensing image super-resolution (SR) aims to enhance image spatial resolution while preserving the accuracy of texture information. Recently, denoising diffusion probabilistic models have overcome issues such as excessive smoothing and modal collapse in generative models, demonstrating excellent performance in image super-resolution tasks. However, due to the diverse and complex nature of land cover types in remote sensing images, existing methods often exhibit performance fluctuations across different scenes, leading to generated details that do not align with ground truth. To address t
- 合写作者：
- Ma, Jiayi,Zhang, Jindou,Wang, Yu,Liao, Zhenghao,Cheng, Gui,Liu, Jun,Guo, Mingqiang,Wu, Liang
- 第一作者：
- Zhang, Zhizheng
- 论文类型：
- 期刊论文
- 通讯作者：
- Shao, Zhenfeng
- 文献类型：
- Article
- 卷号：
- 172
- ISSN号：
- 0031-3203
- 是否译文：
- 否
- 发表时间：
- 2026-04-01