Pansharpening by convolutional neural networks in the full resolution framework

Related tags

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
You might also like...
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

youceF 1 Nov 12, 2021
Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks
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

Qing-Long Zhang 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

Anadi Chaman 46 Dec 19, 2022
《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)
《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

Artёm Komarichev 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

Bogireddy Sai Prasanna Teja Reddy 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

Asutosh Nayak 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

Alex Parinov 694 Nov 23, 2022
Pytorch implementation of AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks
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

mario 124 Sep 16, 2022
PyTorch implementation of
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

Sangchun Ha 24 Nov 24, 2022
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".
GitHub Repository
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021)

RSCD (BS-RSCD & JCD) Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes (CVPR2021) by Zhihang Zhong, Yinqiang Zheng, Imari Sato We co

81 Dec 15, 2022
Dataset used in "PlantDoc: A Dataset for Visual Plant Disease Detection" accepted in CODS-COMAD 2020

PlantDoc: A Dataset for Visual Plant Disease Detection This repository contains the Cropped-PlantDoc dataset used for benchmarking classification mode

Pratik Kayal 109 Dec 29, 2022
Birthday-problem - The birthday problem asks for the probability that, in a set of n randomly chosen people, at least two will share a birthday

Birthday-problem In probability theory, the birthday problem asks for the probab

5 Jan 05, 2023
CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View Rethinking Semantic Segmentation: A Prototype View, Tianfei Zhou, Wenguan Wang, Ender Konukoglu and

Tianfei Zhou 239 Dec 26, 2022
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation

README clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation CVPR 2021 Authors: Suprosanna Shit and Johannes C. Paetzo

110 Dec 29, 2022
Interpretable-contrastive-word-mover-s-embedding

Interpretable-contrastive-word-mover-s-embedding Paper Datasets Here is a Dropbox link to the datasets used in the paper: https://www.dropbox.com/sh/n

0 Nov 02, 2021
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

2s-AGCN Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19 Note PyTorch version should be 0.3! For PyTor

LShi 547 Dec 26, 2022
Unofficial PyTorch implementation of the Adaptive Convolution architecture for image style transfer

AdaConv Unofficial PyTorch implementation of the Adaptive Convolution architecture for image style transfer from "Adaptive Convolutions for Structure-

65 Dec 22, 2022
Code for classifying international patents based on the text of their titles/abstracts

Patent Classification Goal: To train a machine learning classifier that can automatically classify international patents downloaded from the WIPO webs

Prashanth Rao 1 Nov 08, 2022
This script runs neural style transfer against the provided content image.

Neural Style Transfer Content Style Output Description: This script runs neural style transfer against the provided content image. The content image m

Martynas Subonis 0 Nov 25, 2021
Unofficial pytorch implementation of the paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution"

DFSA Unofficial pytorch implementation of the ICCV 2021 paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution" (p

2 Nov 15, 2021
A collection of scripts I developed for personal and working projects.

A collection of scripts I developed for personal and working projects Table of contents Introduction Repository diagram structure List of scripts pyth

Gianluca Bianco 109 Dec 26, 2022
Zero-shot Synthesis with Group-Supervised Learning (ICLR 2021 paper)

GSL - Zero-shot Synthesis with Group-Supervised Learning Figure: Zero-shot synthesis performance of our method with different dataset (iLab-20M, RaFD,

Andy_Ge 62 Dec 21, 2022
Retrieve and analysis data from SDSS (Sloan Digital Sky Survey)

Author: Behrouz Safari License: MIT sdss A python package for retrieving and analysing data from SDSS (Sloan Digital Sky Survey) Installation Install

Behrouz 3 Oct 28, 2022
Repo for "Physion: Evaluating Physical Prediction from Vision in Humans and Machines" submission to NeurIPS 2021 (Datasets & Benchmarks track)

Physion: Evaluating Physical Prediction from Vision in Humans and Machines This repo contains code and data to reproduce the results in our paper, Phy

Cognitive Tools Lab 38 Jan 06, 2023
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

CPT This repository contains code and checkpoints for CPT. CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Gener

fastNLP 341 Dec 29, 2022
Heart Arrhythmia Classification

This program takes and input of an ECG in European Data Format (EDF) and outputs the classification for heartbeats into normal vs different types of arrhythmia . It uses a deep learning model for cla

4 Nov 02, 2022
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".

The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergen

281 Dec 30, 2022