Pansharpening by convolutional neural networks in the full resolution framework

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Deep LearningZ-PNN
Overview

Z-PNN: Zoom Pansharpening Neural Network

Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for Pansharpening based on unsupervised and full-resolution framework training.

Team members

License

Copyright (c) 2021 Image Processing Research Group of University Federico II of Naples ('GRIP-UNINA'). All rights reserved. This software should be used, reproduced and modified only for informational and nonprofit purposes.

By downloading and/or using any of these files, you implicitly agree to all the terms of the license, as specified in the document LICENSE (included in this package)

Prerequisites

All the functions and scripts were tested on Windows and Ubuntu O.S., with these constrains:

  • Python 3.9
  • PyTorch 1.8.1 or 1.10.0
  • Cuda 10.1 and 11.3 (For GPU acceleration).

the operation is not guaranteed with other configurations.

Installation

  • Install Anaconda and git
  • Create a folder in which save the algorithm
  • Download the algorithm and unzip it into the folder or, alternatively, from CLI:
git clone https://github.com/matciotola/Z-PNN
  • Create the virtual environment with the z_pnn_environment.yml
conda env create -n z_pnn_env -f z_pnn_environment.yml
  • Activate the Conda Environment
conda activate z_pnn_env
  • Test it
python main.py -i example/WV3_example.mat -o ./Output_Example -s WV3 -m Z-PNN --coregistration --view_results

Usage

Before to start

To test this algorithm it is needed to create a .mat file. It must contain:

  • I_MS_LR: Original Multi-Spectral Stack in channel-last configuration (Dimensions: H x W x B);
  • I_PAN: Original Panchromatic band, without the third dimension (Dimensions: H x W).

Testing

The easiest command to use the algorithm on full resolution data:

python main.py -i path/to/file.mat -s sensor_name -m method

Several options are possible. Please refer to the parser help for more details:

python main.py -h
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Comments
Releases(v1.0.2)

  • v1.0.2(Jun 17, 2022)

  • v1.0.0(May 30, 2022)

    What's Changed

    • Create codeql-analysis.yml by @matciotola in https://github.com/matciotola/Z-PNN/pull/2

    New Contributors

    • @matciotola made their first contribution in https://github.com/matciotola/Z-PNN/pull/2

    Full Changelog: https://github.com/matciotola/Z-PNN/commits/v1.0.0

    Source code(tar.gz)
    Source code(zip)
Owner
PhD student in "Deep learning for data fusion in remote sensing and beyond".
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