Awesome

DLPan-Toolbox

• This toolbox is related to the paper Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks, IEEE Geoscience and Remote Sensing Magazine, 2022 (see the following reference [1]). Download: [paper].

• This is a deep learning (DL) toolbox for pansharpening, which can be used for training and testing getting the comparison between traditional and DL methods.

Introduction

This toolbox mainly contains two parts: one is the pytorch source codes for the eight DL-based methods presented in the paper (i.e., the folder "01-DL toolbox (Pytorch)"); the other is the Matlab source codes which can simultaneously evaluate the performance of traditional and DL approaches in a uniformed framework ("02-Test toolbox for traditional and DL (Matlab)"). Please see more details:

• 01-DL-toolbox(Pytorch) contains source codes of DL methods, you may check the readme file for the usage.
• 02-Test-toolbox-for-traditional-and-DL(Matlab) contains Matlab source codes (mainly from 'G. Vivone et al., A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods, IEEE GRSM, 2021', see the following reference [2]) for simultaneously evaluating traditional and DL approaches and outputing results, you may check the readme file for the usage.
• 03-Data-Simulation(Matlab) contains Matlab source codes that are patching images to patches for training and validation. Also, you can simulate test examples by this toolbox.

Note that, readers also could check the structure and relationship of these two folders in the following overview figure (also find it in the respository).

Dataset

Due to the copyright issue, the datasets used in this GRSM paper are not available. Therefore, we recommend readers use the following dataset for pansharpening, both training and testing. The following dataset can be directly applied in our DLPan-Toolbox (put the data to the director for training: 01-DL-toolbox(Pytorch)/UDL/Data/pansharpening/training_data/).

• [PanCollection] for multispectral pansharpening
• [HyperPanCollection] for hyperspectral pansharpening

Citation

• [1] If you use this toolbox, please kindly cite our paper:

@ARTICLE{deng2022grsm,
author={L.-J. Deng, G. Vivone, M. E. Paoletti, G. Scarpa, J. He, Y. Zhang, J. Chanussot, and A. Plaza},
booktitle={IEEE Geoscience and Remote Sensing Magazine},
title={Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks},
year={2022},
pages={2-38},
doi={10.1109/MGRS.2020.3019315}
}

• [2] Also, the codes of traditional methods are from the "pansharpening toolbox for distribution", thus please cite the corresponding paper:

@ARTICLE{vivone2021grsm,
  author={Vivone, Gemine and Dalla Mura, Mauro and Garzelli, Andrea and Restaino, Rocco and Scarpa, Giuseppe and Ulfarsson, Magnus O. and   Alparone, Luciano and Chanussot, Jocelyn},
  journal={IEEE Geoscience and Remote Sensing Magazine}, 
  title={A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods}, 
  year={2021},
  volume={9},
  number={1},
  pages={53-81},
  doi={10.1109/MGRS.2020.3019315}
}

Acknowledgement

• We appreciate the great contribution to this toolbox of Xiao Wu and Ran Ran, who are graduate students in UESTC.

License & Copyright

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