Pansharpening by convolutional neural networks in the full resolution framework

Last update: Nov 24, 2022

Related tags

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

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

1 Nov 12, 2021

Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks (paper) By Qing-Long Zhang and Yu-Bin Yang [State Key Laboratory for Novel Software T

199 Jan 8, 2023

《Truly shift-invariant convolutional neural networks》(2021)

Truly shift-invariant convolutional neural networks [Paper] Authors: Anadi Chaman and Ivan Dokmanić Convolutional neural networks were always assumed

46 Dec 19, 2022

《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)

A-CNN: Annularly Convolutional Neural Networks on Point Clouds Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science

44 Feb 24, 2022

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

103 Dec 29, 2022

This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Stock Market Buy/Sell/Hold prediction Using convolutional Neural Network This repo is an attempt to implement the research paper titled "Algorithmic F

136 Dec 28, 2022

Fine-tune pretrained Convolutional Neural Networks with PyTorch

Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A

694 Nov 23, 2022

Pytorch implementation of AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

AngularGrad Optimizer This repository contains the oficial implementation for AngularGrad: A New Optimization Technique for Angular Convergence of Con

124 Sep 16, 2022

PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

24 Nov 24, 2022

Comments

Add license scan report and status
Your FOSSA integration was successful! Attached in this PR is a badge and license report to track scan status in your README.

Below are docs for integrating FOSSA license checks into your CI:

CircleCI

TravisCI

Jenkins

Other
opened by fossabot 1

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

You might also like...

Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

《Truly shift-invariant convolutional neural networks》(2021)

《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Fine-tune pretrained Convolutional Neural Networks with PyTorch

Pytorch implementation of AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

Comments

Add license scan report and status

Releases(v1.0.2)

v1.0.2(Jun 17, 2022)

What's Changed

v1.0.0(May 30, 2022)

What's Changed

New Contributors

Owner

Sequential model-based optimization with a `scipy.optimize` interface

A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Tutorial page of the Climate Hack, the greatest hackathon ever

Self-Supervised Image Denoising via Iterative Data Refinement

Official code for "Towards An End-to-End Framework for Flow-Guided Video Inpainting" (CVPR2022)

这是一个mobilenet-yolov4-lite的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。

Generative Adversarial Networks(GANs)

Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral)

Random Walk Graph Neural Networks

Model Serving Made Easy

ruptures: change point detection in Python

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

Implementation of the Chamfer Distance as a module for pyTorch

Discord-Protect is a simple discord bot allowing you to have some security on your discord server by ordering a captcha to the user who joins your server.

An executor that performs image segmentation on fashion items

This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer.

CSPML (crystal structure prediction with machine learning-based element substitution)

Conditional Gradients For The Approximately Vanishing Ideal