https://github.com/yc-cui/extend-gan
[GRSL 2024] Reconstruction of Large-Scale Missing Data in Remote Sensing Images Using Extend-GAN
https://github.com/yc-cui/extend-gan
deep-learning generative-adversarial-network large-scale pytorch reconstruction remote-sensing
Last synced: about 1 year ago
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[GRSL 2024] Reconstruction of Large-Scale Missing Data in Remote Sensing Images Using Extend-GAN
- Host: GitHub
- URL: https://github.com/yc-cui/extend-gan
- Owner: yc-cui
- License: mit
- Created: 2023-06-25T02:58:46.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-03T08:56:02.000Z (over 2 years ago)
- Last Synced: 2025-03-27T06:30:02.016Z (about 1 year ago)
- Topics: deep-learning, generative-adversarial-network, large-scale, pytorch, reconstruction, remote-sensing
- Language: Python
- Homepage:
- Size: 2.86 MB
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Extend-GAN
This work can be used to extend the boundaries of a high-resolution image to the extent of a given low-resolution reference image:
## Environment
Tested on Ubuntu 20.04. Python version 3.10. Pytorch version 1.13.1.
Create your environment using this command:
```bash
mamba env create -f environment_1.13.1.yaml
```
If you use `conda`, replace `mamba` with `conda`.
## Dataset
Prepare your data and use `util/crop_rs.py` to crop HR and corresponding LR images.
You will get folders following this structure:
```bash
dataset
├─train
│ ├─source
│ │ source1.tif
│ │ source2.tif
│ │ ...
│ │
│ ├─ref
│ │ ref1.tif
│ │ ref2.tif
│ ├...
│
└─test
├─source
│ source1.tif
│ source2.tif
│ ...
│
├─ref
│ ref1.tif
│ ref2.tif
└─...
```
Generate flists in current directory.
```bash
ls -R ${YOUR_ABSOLUTE_PATH} > ${FLIST_NAME}
# for example
ls -R /data/cyc/dataset/train/source/*.tif > train.flist
ls -R /data/cyc/dataset/test/source/*.tif > test.flist
```
The default size is `512`, use `util/crop_256.ipynb` to randomly crop images, if necessary.
## Train
```bash
python train.py --batch_size ${BATCH_SIZE} --train_dataset_name ${YOUR_TRAIN_FLIST} --n_epochs ${TOTAL_EPOCHS}
# for example
python train.py --batch_size 8 --train_dataset_name /data/cyc/dataset/train.flist --n_epochs 2400 > log_42.txt
```
## Test
```bash
python test.py --image_path ${YOUR_TEST_FLIST} --model ${YOUR_GENERATOR_PATH}
# for example
python test.py --image_path /data/cyc/dataset/test.flist --model saved_models/generator_2400.pth
```
## Acknowledgments
We are benefiting a lot from the following projects:
- [Image-Inpainting-Implementations](https://github.com/xyfJASON/Image-Inpainting-Implementations)
- [Boundless-in-Pytorch](https://github.com/recong/Boundless-in-Pytorch)
- [Palette-Image-to-Image-Diffusion-Models](https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models)
If you find this work useful, please cite:
```
@ARTICLE{10413911,
author={Cui, Yongchuan and Liu, Peng and Song, Bingze and Zhao, Lingjun and Ma, Yan and Chen, Lajiao},
journal={IEEE Geoscience and Remote Sensing Letters},
title={Reconstruction of Large-Scale Missing Data in Remote Sensing Images Using Extend-GAN},
year={2024},
volume={21},
number={},
pages={1-5},
keywords={Training;Earth;Artificial satellites;Generative adversarial networks;Spatial resolution;Remote sensing;Image reconstruction;Generative adversarial network (GAN);image reconstruction;remote sensing images;triplet loss},
doi={10.1109/LGRS.2023.3317898}}
```