Weighted Shallow-deep Feature Fusion Network for Pansharpening

Homepage:

https://liangjiandeng.github.io/

https://chengjin.netlify.app/

• Code for paper: "Weighted Shallow-deep Feature Fusion Network for Pansharpening"
• State-of-the-art pansharpening performance

Dependencies and Installation

• Python 3.8 (Recommend to use Anaconda)
• PyTorch > 1.1
• NVIDIA GPU + CUDA
• Python packages: pip install numpy scipy h5py
• TensorBoard

Dataset Preparation

The datasets used in this paper is WorldView-3 (can be downloaded here)). Due to the copyright of dataset, we can not upload the datasets, you may download the data and simulate them according to the paper.

Get Started

Training and testing codes are in 'codes/'. Pretrained model can be found in 'codes/pretrained/'. All codes will be presented after the paper is completed published. Please refer to codes/how-to-run.md for detail description.

WSDF Architecture

ASW architecture is presented below:

Results

Quantitative results

The following quantitative results is generated from WorldView-3 datasets.

All quantitative results can be found in 'results/'.

Visual Results

The following visual results is generated from WorldView-3 datasets.

Citation

@INPROCEEDINGS{wsdfnet,
  author={Jin, Zi-Rong and Zhang, Tian-Jing and Jin, Cheng and Deng, Liang-Jian},
  booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, 
  title={Weighted Shallow-Deep Feature Fusion Network for Pansharpening}, 
  year={2021},
  volume={},
  number={},
  pages={2632-2635},
  doi={10.1109/IGARSS47720.2021.9555127}}

Contact

We are glad to hear from you. If you have any questions, please feel free to contact 2018051403016@std.uestc.edu.cn or open issues on this repository.

License

This project is open sourced under GNU Affero General Public License v3.0.