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WeilKon/
HyperSL

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HyperSL

📎 Overview

HyperSL is designed to:

Learn transferable spectral representations across heterogeneous hyperspectral sensors
Accept arbitrary spectral input dimensions (bands, range)
Generalize without retraining and modifying network structure

🛠️ Features

Masked self-supervised learning strategy tailored for spectral data
Unified spectral token representation
Wavelength-aware positional encoding
Trained on over 300 million spectra from multiple sensors

Official implementation of "HyperSL: A Spectral Foundation Model for Hyperspectral Image Interpretation".

Github:https://github.com/kkweil/HyperSL

🔗 Pretrained Weights

You can download the pretrained weights from Baidu Netdisk:

Link: https://pan.baidu.com/s/11uuzhKs-dtFExlnph1IYTQ
Extraction Code: 27mh

You can download the training dataset from Baidu Netdisk:

Link: https://pan.baidu.com/s/1YP_OMkAf4LytjyowQBq9JQ
Extraction Code: tw7g

📖 Citation

If you find our work useful in your research, please consider citing:

bibtex

@ARTICLE{10981753,
  author={Kong, Weili and Liu, Baisen and Bi, Xiaojun and Yu, Changdong and Li, Xinyao and Chen, Yushi},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={HyperSL: A Spectral Foundation Model for Hyperspectral Image Interpretation}, 
  year={2025},
  volume={63},
  number={5513119},
  pages={1-19},
  keywords={Hyperspectral imaging;Data models;Sensors;Adaptation models;Training;Representation learning;Data mining;Autoencoders;Vectors;Transformers;Cross-scenario;foundation model;hyperspectral image;knowledge transfer},
  doi={10.1109/TGRS.2025.3566205}}

WeilKon/HyperSL