Hyperspectral Image Super-Resolution via Joint Network with Spectral-Spatial Strategy (SRLSGAT)

• Yaxin.Dong, Bo.Yang*, Cong.Liu, Zemin.Geng, Taiping.Wang. Hyperspectral Image Super-Resolution via Joint Network with Spectral-Spatial Strategy, DOI: 10.1080/10095020.2025.2522909, Geo-spatial Information Science (JCR Q1, IF=5.5), 2025

  The paper: https://doi.org/10.1080/10095020.2025.2522909.

  PDF File Download: SRLSGAT-Dongyx1128.pdf

  Implementation of Hyperspectral Image Super-Resolution via Joint Network with Spectral-Spatial Strategy (SRLSGAT) in Pytorch: https://github.com/Dongyx1128/SRLSGAT.

  Hyperspectral image (HSI) super-resolution (SR) faces significant challenges due to the inherent difficulty in acquiring large-scale training data and the complex spectral-spatial relationships in HSIs that conventional deep-learning-based methods often fail to fully exploit. While existing approaches typically stack convolutional layers to increase network depth, they frequently overlook the structured continuity of spectral bands and non-local spatial similarities, resulting in limited performance and overfitting risks. To address these limitations, we propose SRLSGAT, a novel joint spectral-spatial network that combines a vertical-horizontal bi-directional LSTM (VH-BiLSTM) for modeling multi-directional spectral correlations and a multi-adjacent weight matrix graph attention network (MAW-GAT) for capturing non-local patch relationships. Besides, we design a spectral attention mechanism (SpeAM), which dynamically weights remote dependencies through bidirectional spectral sequence analysis, while the graph-based spatial processing adaptively learns patch similarities through optimized edge connections. Extensive experiments on three HSI datasets show that SRLSGAT has better performance relative to SOTA SR methods.

Network Architecture

• The overall network architecture of our proposed SRLSGAT network

  

• Joint Spectral-Spatial Network

  • The Vertical-Horizontal Bi-directional LSTM (VH-BiLSTM)

    

  • The Multi-Adjacent Weight Matrix Graph Attention Network (MAW-GAT)

    

• The Spectral Attention Mechanism (SpeAM)

  

Dependencies

cuda==11.4
numpy==1.15.0
python==2.6.1
scipy==1.1.0
tensorboard==2.0.2
tensorboardX==2.4
thop==0.1.0.post2206102148
torch==1.7.1
torchvision==0.8.2
visdom==0.1.8.9

Installation&Run

• Clone this repo:

  git clone https://github.com/Dongyx1128/SRLSGAT
  cd SRLSGAT

• Train the model

  sh train.sh

• Test the model

  sh test.sh

Citation

  @article{dyx2025SRLSGAT,
  author = {Yaxin Dong and Bo Yang and Cong Liu and Zemin Geng and Taiping Wang},
  title = {Hyperspectral image super-resolution via joint network with spectral-spatial strategy},
  journal = {Geo-spatial Information Science},
  volume = {0},
  number = {0},
  pages = {1-19},
  year  = {2025},
  publisher = {Taylor & Francis},
  doi = {10.1080/10095020.2025.2522909},
  URL = {https://doi.org/10.1080/10095020.2025.2522909},
  eprint = {https://doi.org/10.1080/10095020.2025.2522909},
}

[1] Dong, Yaxin, Bo Yang, Cong Liu, Zemin Geng, and Taiping Wang. 2025. “Hyperspectral Image Super-Resolution via Joint Network with Spectral-Spatial Strategy.” Geo-Spatial Information Science, July, 1–19. doi:10.1080/10095020.2025.2522909.

Acknowledgments

This research was funded by the National Key Research and Development Program of China (No.2023YFD2201702).