Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance
文献类型：期刊论文
| 作者 | Zhang, Yingkai1; Lai, Zeqiang1; Zhang, Tao2; Fu, Ying1; Zhou, Chenghu3,4 |
| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
 |
| 出版日期 | 2025-06-17 |
| 卷号 | N/A |
| 关键词 | Hyperspectral image super-resolution Unaligned RGB guidance Spatial-spectral concordance Two-stage image alignment Feature aggregation Attention fusion |
| ISSN号 | 0920-5691 |
| DOI | 10.1007/s11263-025-02466-8 |
| 产权排序 | 3 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Hyperspectral images (HSIs) super-resolution (SR) aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios. The recent adoption of high-resolution reference images for super-resolution is driven by the poor spatial detail found in low-resolution HSIs, presenting it as a favorable method. However, these approaches cannot effectively utilize information from the reference image, due to the inaccuracy of alignment and its inadequate interaction between alignment and fusion modules. In this paper, we introduce a Spatial-Spectral Concordance Hyperspectral Super-Resolution (SSC-HSR) framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity of the previous approaches. Specifically, to ensure spatial concordance, i.e., align images more accurately across resolutions and refine textures, we construct a Two-Stage Image Alignment (TSIA) with a synthetic generation pipeline in the image alignment module, where the fine-tuned optical flow model can produce a more accurate optical flow in the first stage and warp model can refine damaged textures in the second stage. To enhance the interaction between alignment and fusion modules and ensure spectral concordance during reconstruction, we propose a Feature Aggregation (FA) module and an Attention Fusion (AF) module. In the feature aggregation module, we introduce an Iterative Deformable Feature Aggregation (IDFA) block to achieve significant feature matching and texture aggregation with the fusion multi-scale results guidance, iteratively generating learnable offset. Besides, we introduce two basic spectral-wise attention blocks in the attention fusion module to model the inter-spectra interactions. Extensive experiments on three natural or remote-sensing datasets show that our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code is publicly available to the community (https://github.com/BITYKZhang/SSC-HSR). |
| URL标识 | 查看原文 |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001510316700001 |
| 出版者 | SPRINGER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214531]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Fu, Ying |
| 作者单位 | 1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China; 2.Hangzhou Dianzi Univ, Sch Commun Engn, Hangzhou 310018, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yingkai,Lai, Zeqiang,Zhang, Tao,et al. Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2025,N/A. |
| APA | Zhang, Yingkai,Lai, Zeqiang,Zhang, Tao,Fu, Ying,&Zhou, Chenghu.(2025).Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance.INTERNATIONAL JOURNAL OF COMPUTER VISION,N/A. |
| MLA | Zhang, Yingkai,et al."Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance".INTERNATIONAL JOURNAL OF COMPUTER VISION N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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