FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

Last update: Jan 03, 2023

Overview

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".

We hope the FPGA framework can become a stronger and cleaner baseline for hyperspectral image classification research in the future.

News

2020/05/28, We release the code of FreeNet and FPGA framework.

Features

Patch-free training and inference
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:

@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

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:

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 scripts/freenet_1_0_pavia.sh

2. Salinas

bash scripts/freenet_1_0_salinas.sh

3. GRSS2013

bash scripts/freenet_1_0_grss.sh

License

This source code is released under GPLv3 license.

For commercial use, please contact Prof. Zhong ([email protected]).

Projects using FPGA/FreeNet

TCYB2021: A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification

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Comments

Some issues about the code.

I try to run your code on the Pavia data set.

Traceback (most recent call last): File "d:/FreeNet-master/train.py", line 63, in opts=args.opts) File "D:\software\anaconda\lib\site-packages\simplecv\dp_train.py", line 44, in run traindata_loader = make_dataloader(cfg['data']['train']) File "D:\software\anaconda\lib\site-packages\simplecv\data\data_loader.py", line 9, in make_dataloader raise ValueError('{} is not support now.'.format(dataloader_type)) ValueError: NewPaviaLoader is not support now.

Looking forward to hearing from you!

opened by zhe-meng 2
Test.py module

Hello!

I really appreciate your paper and sharing the code for it. I wonder is there an option to make a test on the trainned network on another image? I saw test dict in config file, but I'm not sure it is implemented for now. Is there any plans for it or will you please suggest how can it be done better?

Thanks!

opened by valeriylo 0
运行 train.py时报错

Traceback (most recent call last): File "D:/论文代码书/代码/高光谱影像分类/全卷积FCN/FreeNet-master/train.py", line 60, in train.run(config_path=args.config_path, File "D:\Anaconda\envs\Pytorch\lib\site-packages\simplecv-0.3.4-py3.8.egg\simplecv\dp_train.py", line 29, in run cfg = config.import_config(config_path) File "D:\Anaconda\envs\Pytorch\lib\site-packages\simplecv-0.3.4-py3.8.egg\simplecv\util\config.py", line 5, in import_config m = importlib.import_module(name='{}.{}'.format(prefix, config_name)) File "D:\Anaconda\envs\Pytorch\lib\importlib_init_.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 1014, in _gcd_import File "", line 991, in _find_and_load File "", line 973, in _find_and_load_unlocked ModuleNotFoundError: No module named 'configs.None'

opened by Zhengpu-L 1
在安装simpleCV时出现报错

您好，我在终端安装simpleCV时出现了以下报错: ERROR: Could not find a version that satisfies the requirement tensorboardX==1.7 (from simplecv) (from versions: none) ERROR: No matching distribution found for tensorboardX==1.7 请问您知道如何解决吗，simpleCV是基于tensorflow框架的吗，但freenet好像是基于pytorch，很抱歉打扰您

opened by wangk98 3

Releases(v1.2)

v1.2(Nov 16, 2021)

It releases a train_roi.tif and val_roi.tif for convenient reproducing the results on GRSS 2013 dataset.

The original GRSS 2013 dataset includes ROI files, which need to be further converted by ENVI software.

Here we share the converted ground truth masks used in our experiments.

Hope it helps.
Source code(tar.gz)
Source code(zip)
train_roi.tif(655.92 KB)
val_roi.tif(650.34 KB)
v1.1(Jul 15, 2020)

fixed known bugs
Source code(tar.gz)
Source code(zip)
v1.0(May 28, 2020)

Source code(tar.gz)
Source code(zip)

Owner

Zhuo Zheng

CV IN RS. Ph.D. Student.

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FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

Related tags

Overview

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

News

Features

Citation

Getting Started

1. Install SimpleCV

2. Prepare datasets

3. run experiments

1. PaviaU

2. Salinas

3. GRSS2013

License

Projects using FPGA/FreeNet

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Comments

Some issues about the code.

Test.py module

运行 train.py时报错

在安装simpleCV时出现报错

Releases(v1.2)

v1.2(Nov 16, 2021)

v1.1(Jul 15, 2020)

v1.0(May 28, 2020)

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