/SSMLP-RPL

Spectral-Spatial MLP Network with Reciprocal Points learning for Open-Set Hyperspectral Image Classification

Primary LanguagePython

SSMLP-RPL

Y. Sun, B. Liu, R. Wang, P. Zhang and M. Dai, "Spectral–Spatial MLP-Like Network With Reciprocal Points Learning for Open-Set Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 5513218, doi: 10.1109/TGRS.2023.3280183.

Requirements

os argparse datetime time importlib scipy torch sklearn

Usage

We provide a demo of the Indian Pines hyperspectral data for open-set classification. Please note that due to the randomness of the parameter initialization, the experimental results might have slightly different from those reported in the paper. Please refer to the paper for more details.

License

Copyright (C) 2022 Yifan Sun

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.

Citation

If the code is helpful to you, please give a star or fork and cite the paper. Thanks! [1] @ARTICLE{10136781, author={Sun, Yifan and Liu, Bing and Wang, Ruirui and Zhang, Pengqiang and Dai, Mofan}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={Spectral–Spatial MLP-Like Network With Reciprocal Points Learning for Open-Set Hyperspectral Image Classification}, year={2023}, volume={61}, pages={1-18}, doi={10.1109/TGRS.2023.3280183}}

References

[1] @article{chen2021adversarial, author={Chen, Guangyao and Peng, Peixi and Wang, Xiangqian and Tian, Yonghong}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={Adversarial Reciprocal Points Learning for Open Set Recognition}, year={2021}, doi={10.1109/TPAMI.2021.3106743} }