README.md

metadata

title: HAT Super-Resolution for Satellite Images
emoji: 🛰️
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.46.1
app_file: app.py
pinned: false

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
• Paper: Activating More Pixels in Image Super-Resolution Transformer
• Authors: Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong

Training Dataset: SEN2NAIPv2

• HuggingFace Dataset: 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:

@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}
}