Pansharpening by convolutional neural networks in the full resolution framework

Last update: Nov 24, 2022

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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

Matteo Ciotola ([email protected]);
Sergio Vitale ([email protected]);
Antonio Mazza ([email protected]);
Giovanni Poggi ([email protected]);
Giuseppe Scarpa ([email protected]).

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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Releases(v1.0.2)

v1.0.2(Jun 17, 2022)
What's Changed

Added the possibility to test the algorithms without the Target-Adaptive modality

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

Pansharpening by convolutional neural networks in the full resolution framework

Related tags

Overview

Z-PNN: Zoom Pansharpening Neural Network

Team members

License

Prerequisites

Installation

Usage

Before to start

Testing

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Releases(v1.0.2)

v1.0.2(Jun 17, 2022)

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v1.0.0(May 30, 2022)

What's Changed

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