EMS-GCN-hyperspectral-image-classification

Demo code of "EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification"

Step 1: compiling cuda files

cd lib
. install.sh ## please wait for about 5 minutes

you can also refer to ESCNet for the compiling process.

Step2: train and test

cd ..
CUDA_VISIBLE_DEVICES='7' python main.py

Step3: record classification result

Citation

If you find this work interesting in your research, please kindly cite:

@ARTICLE{9745164,  
  author={Zhang, Hongyan and Zou, Jiaqi and Zhang, Liangpei},  
  journal={IEEE Transactions on Geoscience and Remote Sensing},   
  title={EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification},   
  year={2022},  
  volume={60},  
  number={},  
  pages={1-16},  
  doi={10.1109/TGRS.2022.3163326}}

Thank you very much! (^▽^)

This code is constructed based on ESCNet and CEGCN, thanks~💕.

If you have any questions, please feel free to contact me (Jiaqi Zou, [email protected]).