SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification, JSTARS, 2021.

Yunsong Li, Bobo Xi, Jiaojiao Li, Rui song, Yuchao Xiao and Jocelyn Chanussot.

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Demo for the paper: SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification.

Fig. 1: The structural schematic diagram of the proposed SGML. The framework is comprised of three parts: adaptive multilevel superpixel segmentation, feature extraction with a novel GSvolution, and a metric learning module.

References

If you find this code helpful, please kindly cite:

[1] Y. Li, B. Xi, J. Li, R. Song, Y. Xiao and J. Chanussot, "SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 609-622, 2022, doi: 10.1109/JSTARS.2021.3135548.

[2] B. Xi, J. Li, Y. Li, R. Song, Y. Xiao, Q. Du, J. Chanussot, “Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2022, doi:10.1109/TNNLS.2022.3158280.

[3] B. Xi, J. Li, Y. Li and Q. Du, "Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2851-2854, doi: 10.1109/IGARSS47720.2021.9553372.

Citation Details

BibTeX entry:

@ARTICLE{Xi_2021JSTARS_SGML,
  author={Li, Yunsong and Xi, Bobo and Li, Jiaojiao and Song, Rui and Xiao, Yuchao and Chanussot, Jocelyn},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification}, 
  year={2022},
  volume={15},
  number={},
  pages={609-622},
  doi={10.1109/JSTARS.2021.3135548}}

@ARTICLE{Xi_2022TNNLS_XGPN,
  author={Xi, Bobo and Li, Jiaojiao and Li, Yunsong and Song, Rui and Xiao, Yuchao and Du, Qian and Chanussot, Jocelyn},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification}, 
  year={2022},
  volume={},
  number={},
  pages={1-15},
  doi={10.1109/TNNLS.2022.3158280}}

@INPROCEEDINGS{Xi_2021IGARSS_GPN,
  author={Xi, Bobo and Li, Jiaojiao and Li, Yunsong and Du, Qian},
  booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, 
  title={Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification}, 
  year={2021},
  volume={},
  number={},
  pages={2851-2854},
  doi={10.1109/IGARSS47720.2021.9553372}}

Licensing

Copyright (C) 2021 Bobo Xi

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.