TPPI: A Novel Network Framework and Model for Efficient Hyperspectral Image Classification.

Introduction

This repository is a PyTorch implementation for hyperspectral image classification. Official implementation of TPPI-Net and Unofficial implementation of Others.

TPPI: A Novel Network Framework and Model for Efficient Hyperspectral Image Classification.
Download: https://www.ingentaconnect.com/contentone/asprs/pers/2022/00000088/00000008/art00012

Contact me:

[email protected]

If you find this code useful in your research, please consider citing:

@article{chen2022tppi,
  title={TPPI: A Novel Network Framework and Model for Efficient Hyperspectral Image Classification},
  author={Chen, Hao and Li, Xiaohua and Zhou, Jiliu and Wang, Yuan},
  journal={Photogrammetric Engineering \& Remote Sensing},
  volume={88},
  number={8},
  pages={535--546},
  year={2022},
  publisher={American Society for Photogrammetry and Remote Sensing}
}

1D CNN:

Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

2D CNN:

   * paper1: Yang, X., Ye, Y., Li, X., Lau, R. Y. K., Zhang, X., & Huang, X. (2018). Hyperspectral image classification with deep learning models. IEEE Transactions on Geoscience and Remote Sensing, 56(9), 5408–5423. https://doi.org/10.1109/TGRS.2018.2815613
   * paper2: Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

3D CNN:

   * paper1: Ben Hamida, A., Benoit, A., Lambert, P., & Ben Amar, C. (2018). 3-D deep learning approach for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(8), 4420–4434. https://doi.org/10.1109/TGRS.2018.2818945
   * paper2: Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 6232–6251. https://doi.org/10.1109/TGRS.2016.2584107

HybridSN:

Roy, S. K., Krishna, G., Dubey, S. R., & Chaudhuri, B. B. (2020). HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 17(2), 277–281. https://doi.org/10.1109/LGRS.2019.2918719

SSRN:

Zhong, Z., Li, J., Luo, Z., & Chapman, M. (2018). Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework. IEEE Transactions on Geoscience and Remote Sensing, 56(2), 847–858. https://doi.org/10.1109/TGRS.2017.2755542

pResNet:

Paoletti, M. E., Haut, J. M., Fernandez-Beltran, R., Plaza, J., Plaza, A. J., & Pla, F. (2019). Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 740–754. https://doi.org/10.1109/TGRS.2018.2860125

SSAN:

Sun, H., Zheng, X., Lu, X., & Wu, S. (2020). Spectral-Spatial Attention Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5), 3232–3245. https://doi.org/10.1109/TGRS.2019.2951160

Implemented networks

TPPI-Net

TPPP-Nets (Notice: I modified them slightly to make them have higher classification accuracy. If you are interested in these models, please refer to their official implementations):

1. 1D CNN: source code: I can't find it -.-

2. 2D CNN: source code of paper1: -.- source code of paper2: -.-

3. 3D CNN: source code of paper1: https://github.com/AminaBh/3D_deepLearning_for_hyperspectral_images source code of paper2: -.-

4. HybridSN: source code: https://github.com/gokriznastic/HybridSN

5. SSAN: source code: I got the implementation of SSAN by contacting Prof. Zheng: [email protected]

6. pResNet: source code: https://github.com/mhaut/pResNet-HSI

7. SSRN: source code: https://github.com/zilongzhong/SSRN

Usage

Setup config file

Modify ‘configs/config.yml’

Data set preparation

python create_dataset.py

Training

python train.py

Evaluation

For TPPI-Nets, run:

python TPPI_predict.py

For TPPP-Nets, run:

python TPPP_predict.py