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https://github.com/Z-Zheng/FreeNet
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification (TGRS 2020) https://ieeexplore.ieee.org/document/9007624
https://github.com/Z-Zheng/FreeNet
computer-vision convolutional-neural-network deep-learning geospatial hyperspectral-image-classification remote-sensing
Last synced: 2 days ago
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FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification (TGRS 2020) https://ieeexplore.ieee.org/document/9007624
- Host: GitHub
- URL: https://github.com/Z-Zheng/FreeNet
- Owner: Z-Zheng
- License: gpl-3.0
- Created: 2019-07-05T09:31:10.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-22T05:45:30.000Z (9 months ago)
- Last Synced: 2024-03-15T02:39:34.900Z (4 months ago)
- Topics: computer-vision, convolutional-neural-network, deep-learning, geospatial, hyperspectral-image-classification, remote-sensing
- Language: Python
- Homepage:
- Size: 30.3 KB
- Stars: 99
- Watchers: 4
- Forks: 29
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-hyperspectral-image-classification - FreeNet - Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification". (3 Code / 3.2 Comparison methods of our proposed LESSFormer methods)
README
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-casi?p=fpga-fast-patch-free-global-learning-1)
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-pavia?p=fpga-fast-patch-free-global-learning-1)
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-salinas-1?p=fpga-fast-patch-free-global-learning-1)
[](https://www.gnu.org/licenses/gpl-3.0)
FPGA & FreeNet
Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
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"](https://ieeexplore.ieee.org/document/9007624).
We hope the FPGA framework can become a stronger and cleaner baseline for hyperspectral image classification research in the future.
## News
1. 2020/05/28, We release the code of FreeNet and FPGA framework.
## Features
1. Patch-free training and inference
2. Fully end-to-end (w/o preprocess technologies, such as dimension reduction)
## Citation
If you use FPGA framework or FreeNet in your research, please cite the following paper:
```text
@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}}
}
```
## Getting Started
### 1. Install SimpleCV
```bash
pip install --upgrade git+https://github.com/Z-Zheng/SimpleCV.git
```
### 2. Prepare datasets
It is recommended to symlink the dataset root to `$FreeNet`.
The project should be organized as:
```text
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
```
### 3. run experiments
#### 1. PaviaU
```bash
bash scripts/freenet_1_0_pavia.sh
```
#### 2. Salinas
```bash
bash scripts/freenet_1_0_salinas.sh
```
#### 3. GRSS2013
```bash
bash scripts/freenet_1_0_grss.sh
```
### License
This source code is released under [GPLv3](http://www.gnu.org/licenses/) license.
For commercial use, please contact Prof. Zhong ([email protected]).