Explicit High-level Semantic Network for Domain Generalization in Hyperspectral Image Classification

This is the official code of EHSnet. Paper web page: Explicit High-level Semantic Network for Domain Generalization in Hyperspectral Image Classification.

Citation

@ARTICLE{10750220,
  author={Wang, Xusheng and Dong, Shoubin and Zheng, Xiaorou and Lu, Runuo and Jia, Jianxin},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Explicit High-level Semantic Network for Domain Generalization in Hyperspectral Image Classification}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TGRS.2024.3495765}}

Dataset

You can download the Pavia and Houston dataset on here.

As for the XS dataset, please contact the authors below:

• Jia J, Wang Y, Zheng X, et al. Design, Performance, and Applications of AMMIS: A Novel Airborne Multi-Modular Imaging Spectrometer for High-Resolution Earth Observations[J]. Engineering, 2024..

The dataset directory should look like this:

datasets
├── Houston
│   ├── Houston13.mat
│   ├── Houston13_7gt.mat
│   ├── Houston18.mat
│   └── Houston18_7gt.mat
├── Pavia
│   ├── paviaC.mat
│   ├── paviaC_7gt.mat
│   ├── paviaU.mat
│   └── paviaU_7gt.mat
└── XS
    ├── XS_0.mat
    ├── XS_gt_0.mat
    ├── XS_1.mat
    └── XS_gt_1.mat

Requirement

• CUDA Version: 11.3
• PyTorch version: 1.11.0
• Python version: 3.8.10
• You can download the the CLIP pre-training weight ViT-B-32.pt here.

Usage

• For Houston dataset, you can run the train.py with python train.py --dataset Houston --alpha 0.1 --beta 1e+0 --re_ratio 5
• For Pavia dataset, you can run the train.py with python train.py --dataset Pavia --alpha 0.7 --beta 1e+0 --re_ratio 1
• For XS dataset, you can run the train.py with python train.py --dataset XS --alpha 0.3 --beta 1e-1 --re_ratio 1 --training_sample_ratio 0.1 --num_epoch 100

Acknowledgment

Our code is based on the method of LDGnet. Thanks for their work.