| 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 |
| mmnet-IJCV | Memory-augmented Deep Unfolding Network for Guided Image Super-resolution |
If you have any questions, please contact us (manman@mail.ustc.edu.cn)
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.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
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
You can install the necessary packages using pip:
pip install torch numpy matplotlib opencv-python pyyamlBefore 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.
To train the model, you can run the following command:
python main.py
To test the trained pan-sharpening model, you can run the following command:
python test.py
python py-tra/demo_deep_methods.py
The configuration options are stored in the option.yaml file. Here is an explanation of each of the options:
- algorithm: The model for training
log_dir: The location where the log files will be stored.
checkpoint: The location to store the model weights.
data_dir_train: The location of the training data.data_dir_eval: The location of the test data.
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.
algorithm: The algorithm to use for testing.type: The type of testing, eithertestoreval.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.
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.
schedule.lr: The learning rate.schedule.decay: The learning rate decay.schedule.gamma: The learning rate decay factor.schedule.optimizer: The optimizer to use, eitherADAM,SGD, orRMSprop.schedule.momentum: The momentum for theSGDoptimizer.