https://github.com/immortal13/EMS-GCN-hyperspectral-image-classification

Demo code of "EMS-GCN: An End-to-End Mixhop Superpixel-Based Graph Convolutional Network for Hyperspectral Image Classification"
https://github.com/immortal13/EMS-GCN-hyperspectral-image-classification

Last synced: 2 days ago
JSON representation

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

• Host: GitHub
• URL: https://github.com/immortal13/EMS-GCN-hyperspectral-image-classification
• Owner: immortal13
• Created: 2023-07-14T08:22:53.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2023-12-04T07:25:07.000Z (7 months ago)
• Last Synced: 2024-03-15T02:39:38.994Z (4 months ago)
• Language: C++
• Size: 25.4 KB
• Stars: 9
• Watchers: 2
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Lists

• awesome-hyperspectral-image-classification - EMS-GCN

README

        
# 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](https://github.com/Bobholamovic/ESCNet) for the compiling process.

## Step2: train and test

```

cd ..

CUDA_VISIBLE_DEVICES='7' python main.py

```

## Step3: record classification result

![image](https://github.com/immortal13/EMS-GCN-hyperspectral-image-classification/assets/44193495/da4a1091-3180-4fc3-b4dc-7926b2835819)

## 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](https://github.com/Bobholamovic/ESCNet) and [CEGCN](https://github.com/qichaoliu/CNN_Enhanced_GCN), thanks~💕.

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