Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense Sampling and Chain Training
- Fanen Meng, Sensen Wu, Yadong Li, Zhe Zhang, Tian Feng, Renyi Liu, Zhenhong Du
- IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-22
- https://ieeexplore.ieee.org/document/10375518
- checkpoint: https://pan.baidu.com/s/1eUANo6besXp5HctEqqRVOA?pwd=0115 or https://drive.google.com/drive/folders/1_ncUya30a-AwaDXkys56AMj2m8eSJjfH?usp=sharing
Our folder structure is as follows:
├── dataset (dataset used by SRADSGAN)
│ ├── sradsgan
│ │ ├── AID
│ │ ├── DOTA
│ │ ├── LoveDA
│ │ ├── RSSCN7_2800
│ │ ├── SECOND
│ │ ├── UCMerced_LandUse
├── SRADSGAN (code)
│ ├── data
│ ├── GDP_x0
│ ├── img
│ │ ├── GF2_HR.tif
│ │ ├── GF2_LR.tif
│ │ ├── Sentinel2.tif
│ ├── model
│ │ ├── amssrn.py (amssrn model)
│ │ ├── drcan.py (drcan model)
│ │ ├── dssr.py (dssr model)
│ │ ├── edsr.py (edsr model)
│ │ ├── hat.py (hat model)
│ │ ├── ndsrgan.py (ndsrgan model)
│ │ ├── sradsgan.py (sradsgan model)
│ │ ├── sragan.py (sragan model)
│ │ ├── srgan.py (srgan model)
│ ├── utils
│ ├── main_amssrn.py
│ ├── main_drcan.py
│ ├── main_dssr.py
│ ├── main_edsr.py
│ ├── main_hat.py
│ ├── main_ndsrgan.py
│ ├── main_sradsgan.py
│ ├── main_sragan.py
│ ├── main_srgan.py
│ ├── Scene_classification_mfe.py (scene classification)
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SRADSGAN (traditional generative model architecture)
The HAT model uses python 3.8, pytorch 1.9, tensorflow-gpu 2.1.0, and the environment of other models is in requirements.txt
pip install -r requirements.txt
We used six datasets to train our model. After secondary processing, we obtained a total of about 35,000 images of 216*216 size.
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Train
- ["AID", "DOTA", "LoveDA", "RSSCN7_2800", "SECOND"]
-
Test
-
["UCMerced_LandUse"]
-
Link: https://drive.google.com/drive/folders/1e4VRUgFL4bDRfrb0CS6-Y9f7bTZhBqiV?usp=sharing or
https://pan.baidu.com/s/1cXkKu-CI6Q8EF_7Kbfxodg?pwd=w5t8 提取码:w5t8
- Prepare environment, datasets and code.
- Run training / evaluation code. The code is for training on 1 GPU.
# sradsgan
cd SRADSGAN
python main_sradsgan.py
---------------------------------------------------------------
net.train() # train
net.mfeNew_validate() # test
net.mfeNew_validateByClass() # classes test
net.mfe_test_single() # infer
---------------------------------------------------------------
# GDP_x0
cd GDP_x0
python sr_mfe.py -p train -c config/gdp_train_27_216.json # train
python sr_mfe.py -p val -c config/gdp_test_27_216.json # test
python sr_mfe.py -p val -c config/gdp_GF2_x3.json # infer
Fig. 5. Visualization of different methods on UC Merced dataset. From (a) to (e) are x2, x3, x4, x8 and x9 SR results, respectively.
Fig. 8. 3-time super-resolution results of different methods on GaoFen-2 remote sensing image.
Fig. 9. 9-time super-resolution results on Sentinel-2 remote sensing image without downsampling.
If our code helps your research or work, please consider citing our paper.
F. Meng et al., "Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense
Sampling and Chain Training," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-22, 2024, Art no. 5400822,
doi: 10.1109/TGRS.2023.3344112.