Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects

Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, and Jocelyn Chanussot

Sample Disjoint Dataset

Get the disjoint dataset (Trento11x11 folder) from Google Drive.

The repository contains the implementations for Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects.

Models

Currently, the following traditional machine learning methods are available:

• [x] RF
• [x] MLR
• [x] SVM
• [x] MLP
• [x] RNN
• [x] LSTM
• [x] GRU

Currently, the following deep learning methods are available:

• [x] CNN-1D
• [x] CNN-2D
• [x] CNN-3D
• [x] HybridSN
• [x] MorphCNN

Currently, the following convolutional feature extraction methods are available:

• [x] G2C-Conv2D
• [x] G2C-Conv3D

If you have questions or suggestions, please feel free to open an issue. Please cite as:

@article{ahmad2021hyperspectral,
  title={Hyperspectral Image Classification--Traditional to Deep Models: A Survey for Future Prospects},
  author={Muhammad Ahmad, and Sidrah Shabbir, and Swalpa Kumar Roy, and Danfeng Hong, and Xin Wu, and Jing Yao, and Adil Mehmood Khan, and Manuel Mazzara, and Salvatore Distefano, and Jocelyn Chanussot},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2022},
  volume={15},
  pages={968-999},
  doi={10.1109/JSTARS.2021.3133021},
  publisher={IEEE}
}

Acknowledgement

Part of this code is from a implementation of Classification of HSI using CNN by Gopal Krishna, Behnood Rasti and Jaun Mario Haut.