The code in this toolbox implements the "A Semi-Supervised Domain Alignment Transformer for Hyperspectral Images Change Detection".
Please kindly cite the papers if this code is useful and helpful for your research.
@article{10260309,
title={A Semi-Supervised Domain Alignment Transformer for Hyperspectral Images Change Detection},
author={Wang, Yanheng and Sha, Jianjun and Gao, Lianru and Zhang, Yonggang and Rong, Xianhui and Zhang, Ce},
journal={IEEE Trans. Geosci. Remote Sens.},
year={2023},
volume={},
number={},
pages={1-11},
note = {DOI: 10.1109/TGRS.2023.3317919}
}
This code requires a Python Package:torch 1.8.1+cu111, numpy 1.22.4+mkl, scikit-learn 0.23.2, scipy 1.5.2, matplotlib 3.3.2
Here an example experiment is given by using Farmland Dateset. Directly run train.py functions with different network parameter settings to produce the results.
python train.py --dataset='farmland' --ae_number=2000 --cd_number=100
MIT License
Copyright (c) 2023 yanhengwang-heu
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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