| --- |
| title: HAT Super-Resolution for Satellite Images |
| emoji: 🛰️ |
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| sdk: gradio |
| sdk_version: 5.46.1 |
| app_file: app.py |
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| --- |
| |
| # HATSAT - Super-Resolution for Satellite Images |
|
|
| This Hugging Face Space demonstrates a fine-tuned **Hybrid Attention Transformer (HAT)** model for satellite image super-resolution. The model performs 4x upscaling of satellite imagery, enhancing the resolution while preserving important geographical and structural details. |
|
|
| ## Model Details |
|
|
| - **Architecture**: HAT (Hybrid Attention Transformer) |
| - **Upscaling Factor**: 4x |
| - **Input Channels**: 3 (RGB) |
| - **Training**: Fine-tuned on satellite imagery dataset |
| - **Base Model**: Pre-trained HAT model from ImageNet |
|
|
| ## Model Configuration |
|
|
| - **Window Size**: 16 |
| - **Embed Dimension**: 180 |
| - **Depths**: [6, 6, 6, 6, 6, 6] |
| - **Number of Heads**: [6, 6, 6, 6, 6, 6] |
| - **Compress Ratio**: 3 |
| - **Squeeze Factor**: 30 |
| - **Overlap Ratio**: 0.5 |
|
|
| ## Usage |
|
|
| 1. Upload a satellite image (RGB format) |
| 2. The model will automatically upscale it by 4x |
| 3. Download the enhanced high-resolution result |
|
|
| ## Training Details |
|
|
| The model was fine-tuned using: |
| - **Loss Function**: L1Loss |
| - **Optimizer**: Adam (lr=2e-5) |
| - **Training Iterations**: 20,000 |
| - **Scheduler**: MultiStepLR with milestones at [10000, 50000, 100000, 130000, 140000] |
|
|
| ## Applications |
|
|
| This model is particularly useful for: |
| - Enhancing low-resolution satellite imagery |
| - Geographic analysis and mapping |
| - Environmental monitoring |
| - Urban planning and development |
| - Agricultural monitoring |
|
|
| ## Technical Implementation |
|
|
| The model implements several key architectural components: |
| - **Hybrid Attention Blocks (HAB)**: Combining window-based and overlapping attention |
| - **Overlapping Cross-Attention Blocks (OCAB)**: For enhanced feature extraction |
| - **Residual Hybrid Attention Groups (RHAG)**: Stacked attention layers with residual connections |
| - **Channel Attention Blocks (CAB)**: For feature refinement |
|
|
| ## Performance |
|
|
| The model has been trained for 20,000 iterations with careful monitoring of PSNR and SSIM metrics on satellite imagery validation data. |
|
|
| ## Acknowledgments |
|
|
| This model is a fine tuned version of **HAT (Hybrid Attention Transformer)** and trained on the **SEN2NAIPv2** dataset. |
|
|
| ### Base Model: HAT |
| - **GitHub Repository**: [https://github.com/XPixelGroup/HAT](https://github.com/XPixelGroup/HAT) |
| - **Paper**: [Activating More Pixels in Image Super-Resolution Transformer](https://arxiv.org/abs/2205.04437) |
| - **Authors**: Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong |
|
|
| ### Training Dataset: SEN2NAIPv2 |
| - **HuggingFace Dataset**: [https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2](https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2) |
| - **Description**: High-resolution satellite imagery dataset for super-resolution tasks |
|
|
| ## Citation |
|
|
| If you use this model in your research, please cite both the original HAT paper and the SEN2NAIPv2 dataset: |
|
|
| ```bibtex |
| @article{chen2023hat, |
| title={Activating More Pixels in Image Super-Resolution Transformer}, |
| author={Chen, Xiangyu and Wang, Xintao and Zhou, Jiantao and Qiao, Yu and Dong, Chao}, |
| journal={arXiv preprint arXiv:2205.04437}, |
| year={2022} |
| } |
| |
| @misc{sen2naipv2, |
| title={SEN2NAIPv2: A Large-Scale Dataset for Satellite Image Super-Resolution}, |
| author={TACO Foundation}, |
| year={2024}, |
| url={https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2} |
| } |
| ``` |
