RESTORE-DiT is a novel Diffusion-based framework for Satellite Image Time Series (SITS) reconstruction. Our work firstly promotes the sequence-level optical-SAR fusion through a diffusion framework. Paper.

Inspired by the great success of diffusion models in image and video generation, we approach the time series reconstruction problem from the perspective of conditional generation. Conditioned on SAR image time series and date information, RESTORE-DiT achieves superior reconstruction performance for highly-dynamic land surface (e.g. vegetations) under persist cloud cover (as shown below).

Fig. 1. Structure of RESTORE-DiT framework. The noisy cloudy optical time series is iteratively denoised by Denoising Transformer under the condition of SAR and date.

• Code and configuration for training and test dataset at France site.
• Code for Denoising Transformer.
• Training codes of RESTORE-DiT.
• Evaluation codes of RESTORE-DiT.

pip install -r requirements.txt

1. Dataset download

   Download the original PASTIS-R dataset here.

2. Generate cloud masks

   The model is trained on cloud-free image time series with simulated masks. Cloud/shadow detection is necessary to remove cloudy images in each time series. CloudSEN12 is used for cloud/shadow detection.

   Modify the data folder of PASTIS-R in CloudDetection.py and generate the real cloud masks of PASTIS-R dataset. You may need to install necessary packages like segmentation_models_pytorch and geopandas to run CloudDetection.py.

3. Record cloudy frames

   Based on the obtained cloud masks [T,1,H,W], Indexes of cloudy frames for each sample are recorded in a json file, which will be used for pre-processing in PASTISDataset.py. We provide the json file for the PASTIS-R dataset. You can simply place it to the root of PASTIS-R dataset for training.

python run_train_PASTIS.py ./configs/config_PASTIS_train.yaml --save_dir ./results/

This command will create a ./results/START_TIME path, which saves the training configs and models. The START_TIME is the folder named based on the time you start training, which could be shown as "2025-06-17_18-00".

python run_eval.py config_yaml_path SDT --test-data.test-config ./configs/config_PASTIS_test_simulation.yaml --checkpoint pth_model_path --inference_steps 1

To use the command above for evaluation on the test set of PASTIS-R, YOU NEED TO:

1. Replace the config_yaml_path to your specific config.yaml path in results folder, which could be like ./results/2025-06-17_18-00/config.yaml.
2. Replace the pth_model_path to your specific saved model path in results folder, which could be like ./results/2025-06-17_18-00/checkpoints/Model_best.pth.

If you find our method useful in your research, please cite with:

@ARTICLE{RESTORE-DiT,
  author={Shu, Qidi and Zhu, Xiaolin and Xu, Shuai and Wang, Yan and Liu, Denghong},
  journal={Remote Sensing of Environment}, 
  title={RESTORE-DiT: Reliable satellite image time series reconstruction by multimodal sequential diffusion transformer}, 
  year={2025},
  volume={328},
  number={114872},
}

Thanks for these excellent works: U-TILISE, VDT, DiT, DiffCR, PASTIS-R.