|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
## Model Card for UNet-6depth-shuffle: `venkatesh-thiru/s2l8h-UNet-6depth-shuffle` |
|
|
|
|
|
### Model Description |
|
|
|
|
|
The UNet-6depth-shuffle model harmonizes Landsat-8 and Sentinel-2 imagery by improving the spatial resolution of Landsat-8 images. This model uses Landsat-8 multispectral and pan-chromatic images to produce outputs that match the Sentinel-2's spectral and spatial characteristics. |
|
|
|
|
|
### Model Architecture |
|
|
|
|
|
This UNet model features 6 depth levels and incorporates a shuffling mechanism to enhance image resolution and spectral accuracy. The depth and shuffling operations are tailored to achieve high-quality transformations, ensuring the output images closely resemble Sentinel-2 data. |
|
|
|
|
|
### Usage |
|
|
|
|
|
```python |
|
|
from transformers import AutoModel |
|
|
|
|
|
# Load the UNet-6depth-shuffle model |
|
|
model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-shuffle", trust_remote_code=True) |
|
|
|
|
|
# Harmonize Landsat-8 images |
|
|
l8up = model(l8MS, l8pan) |
|
|
``` |
|
|
### Where: |
|
|
|
|
|
l8MS - Landsat Multispectral images (L2 Reflectances) |
|
|
|
|
|
l8pan - Landsat Pan-Chromatic images (L1 Reflectances) |
|
|
|
|
|
### Applications |
|
|
Water quality assessment |
|
|
|
|
|
Urban planning |
|
|
|
|
|
Climate monitoring |
|
|
|
|
|
Disaster response |
|
|
|
|
|
Infrastructure oversight |
|
|
|
|
|
Agricultural surveillance |
|
|
|
|
|
### Limitations |
|
|
Minor limitations may arise in regions with different spectral properties or under extreme environmental conditions. |
|
|
|
|
|
### Reference |
|
|
For more details, refer to the publication: 10.1016/j.isprsjprs.2024.04.026 |
|
|
|
