zhouchuanxu / Awaresome-pansharpening

pan-sharpening Team-zhouman

model	paper
panformer-AAAI	Pan-sharpening with Customized Transformer and Invertible Neural Network
GCPNet-TGRS	When Pansharpening Meets Graph Convolution Network and Knowledge Distillation
band_aware-TCI	PAN-guided band-aware multi-spectral feature enhancement for Pan-sharpening
distill-TGRS	Effective Pan-sharpening by Multi-Scale Invertible Neural Network and Heterogeneous Task Distilling
pan_unfolding-CVPR	Memory-augmented Deep Conditional Unfolding Network for Pan-sharpening
MutInf-CVPR	Mutual Information-driven Pan-sharpening
SFITNET-ECCV	Spatial-Frequency Domain Information Integration for Pan-sharpening
SFINet++-TPAMI	A General Spatial-Frequency Learning Framework for Satellite Image Pansharpening
mmnet-IJCV	Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
LHFnet-ACM MM	Adaptively Learning Low-high Frequency Information Integration for Pan-sharpening
NormNet-ACM MM	Normalization-based Feature Selection and Restitution for Pan-sharpening
CINet-TGRS	Information Compensation and Integration Network for Pan-sharpening

If you have any questions, please contact us ([e-mail])

This repository contains the implementation of the various algorithm for super-resolution of remote sensing images. The algorithm is trained using a deep neural network architecture and is implemented using PyTorch.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

In order to run this implementation, you need to have the following software and libraries installed:

• Python 3.7 or later
• PyTorch 1.6 or later
• CUDA (if using GPU)
• NumPy
• Matplotlib
• OpenCV
• PyYAML

Installing

You can install the necessary packages using pip:

pip install torch numpy matplotlib opencv-python pyyaml

Prepare Dataset

All the training/testing data are avalibal at Baidu Disk code: i1dk

If you use this datasets, please cite our paper to acknowledge our efforts.

Configuration

Before training the model, you need to configure the following options in the option.yaml file:

• log_dir: the directory to store the training log files.
• checkpoint: the directory to store the trained model parameters.
• data_dir_train: the directory of the training data.
• data_dir_eval: the directory of the evaluation data.

Training the Model

To train the model, you can run the following command:

python main.py

Testing the Model

To test the trained pan-sharpening model, you can run the following command:

python test.py
python py-tra/demo_deep_methods.py

Configuration

The configuration options are stored in the option.yaml file. Here is an explanation of each of the options:

algorithm

• algorithm: The model for training

Logging

• log_dir: The location where the log files will be stored.

Model Weights

• checkpoint: The location to store the model weights.

Training Data

• data_dir_train: The location of the training data.
• data_dir_eval: The location of the test data.

Pretrain

• pretrained: Whether to use a pretrained model.
• pre_sr: The location of the pretrained model.
• pre_folder: The location where the pretrained models are stored.

Testing

• algorithm: The algorithm to use for testing.
• type: The type of testing, either test or eval.
• data_dir: The location of the test data.
• source_ms: The source of the multi-spectral data.
• source_pan: The source of the panchromatic data.
• model: The location of the model to use for testing.
• save_dir: The location to save the test results.

Data Processing

• upscale: The upscale factor.
• batch_size: The size of each batch.
• patch_size: The size of each patch.
• data_augmentation: Whether to use data augmentation.
• n_colors: The number of color channels.
• rgb_range: The range of the RGB values.
• normalize: Whether to normalize the data.

Training Hyperparameters

• schedule.lr: The learning rate.
• schedule.decay: The learning rate decay.
• schedule.gamma: The learning rate decay factor.
• schedule.optimizer: The optimizer to use, either ADAM, SGD, or RMSprop.
• schedule.momentum: The momentum for the SGD optimizer.