YonghaoXu / RSEN

[IEEE TNNLS 2022] Robust Self-Ensembling Network for Hyperspectral Image Classification

Robust Self-Ensembling Network for Hyperspectral Image Classification

This is the official PyTorch implementation of the paper Robust Self-Ensembling Network for Hyperspectral Image Classification

Preparation

• Install required packages: pip install -r requirements.txt

• Download the Pavia University image and the corresponding annotations. Put these files into the dataset folder.

Usage

• Data Preparation:

$ python sample_generation.py

The default training set is generated by randomly selecting 30 labeled samples from each category.

You can change parameter --num_label to check the performance in other training scenarios.

• Performance Evaluation:

$ CUDA_VISIBLE_DEVICES=0 python train_RSEN.py

Paper

Robust Self-Ensembling Network for Hyperspectral Image Classification

Please cite our paper if you find it useful for your research.

@article{rsen,
  title={Robust Self-Ensembling Network for Hyperspectral Image Classification}, 
  author={Xu, Yonghao and Du, Bo and Zhang, Liangpei},
  journal={IEEE Trans. Neural Netw. Learn. Syst.}, 
  volume={},
  number={},
  pages={},
  year={2022},
  doi={10.1109/TNNLS.2022.3198142}}
}