Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du.

Code for paper: Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification.

Training and Test Process

Please simply run 'SSGPN_IP.py' to reproduce the SSGPN results on IndianPines data set. The groundtruth and the obtained classification map are shown below. We have successfully test it on Ubuntu 16.04 with Tensorflow 1.13.1 and GTX 1080 Ti GPU.

References

If you find this code helpful, please kindly cite:

[1] 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
[2] B. Xi, J. Li, Y. Li, R. Song, Y. Shi, S. Liu, Q. Du "Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3683-3700, 2020, doi: 10.1109/JSTARS.2020.3004973.

Citation Details

BibTeX entry:

@INPROCEEDINGS{Xi2021IGARSS,
  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}}

@ARTICLE{Xi2020JSTARS,
  author={B. {Xi} and J. {Li} and Y. {Li} and R. {Song} and Y. {Shi} and S. {Liu} and Q. {Du}},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
  title={Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification}, 
  year={2020},
  volume={13},
  number={},
  pages={3683-3700},
  doi={10.1109/IGARSS47720.2021.9553372}}

Licensing

Copyright (C) 2020 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.