by Zhuo Zheng, Yanfei Zhong, Ailong Ma and Liangpei Zhang
This is an official implementation of FPGA framework and FreeNet in our TGRS 2020 paper "FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification".
We hope the FPGA framework can become a stronger and cleaner baseline for hyperspectral image classification research in the future.
- 2020/05/28, We release the code of FreeNet and FPGA framework.
- Patch-free training and inference
- Fully end-to-end (w/o preprocess technologies, such as dimension reduction)
If you use FPGA framework or FreeNet in your research, please cite the following paper:
@article{zheng2020fpga,
title={FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification},
author={Zheng, Zhuo and Zhong, Yanfei and Ma, Ailong and Zhang, Liangpei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2020},
publisher={IEEE},
note={doi: {10.1109/TGRS.2020.2967821}}
}
pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.gitIt is recommended to symlink the dataset root to $FreeNet.
The project should be organized as:
FreeNet
├── configs // configure files
├── data // dataset and dataloader class
├── module // network arch.
├── scripts
├── pavia // data 1
│ ├── PaviaU.mat
│ ├── PaviaU_gt.mat
├── salinas // data 2
│ ├── Salinas_corrected.mat
│ ├── Salinas_gt.mat
├── GRSS2013 // data 3
│ ├── 2013_IEEE_GRSS_DF_Contest_CASI.tif
│ ├── train_roi.tif
│ ├── val_roi.tif
bash scripts/freenet_1_0_pavia.shbash scripts/freenet_1_0_salinas.shbash scripts/freenet_1_0_grss.shThis source code is released under GPLv3 license.
For commercial use, please contact Prof. Zhong (zhongyanfei@whu.edu.cn).