Skip to content

BUCT-Vision/Two-branch-CNN-Multisource-RS-classification

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Two-branch-CNN-Multisource-RS-classification

This example implements the paper Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

A two-branch CNN architecture for feasture fusion with HSI and other remote scensing imagery. Reach a quite high classification accuracy. Evaluated on the dataset of Houston, Trento, Salinas and Pavia.

Prerequisites

  • System Ubuntu 14.04 or upper
  • Python 2.7 or 3.6
  • Packages
pip install -r requirements.txt

Usage

dataset utilization

Please modify line 10-22 in data_util.py for the dataset details.

Training

  1. Train HSI
python main.py --train hsi --epochs 20 --modelname ./logs/weights/hsi.h5
  1. Train LiDAR/VIS
python main.py --train lidar --epochs 20 --modelname ./logs/weights/lidar.h5
  1. Train two branches
python main.py --train finetune --epochs 20 --modelname ./logs/weights/model.h5

Results

All the results are cited from original paper. More details can be found in the paper.

dataset Kappa OA
Houston 0.8698 87.98%
Trento 0.9681 97.92%
Pavia 0.9883 99.13%
Salinas 0.9745 97.72%

Citation

@article{xu2017multisource,
  title={Multisource Remote Sensing Data Classification Based on Convolutional Neural Network},
  author={Xu, Xiaodong and Li, Wei and Ran, Qiong and Du, Qian and Gao, Lianru and Zhang, Bing},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017},
  publisher={IEEE}
}

TODO

  1. pytorch version.
  2. more flexiable dataset utilization

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages