File size: 3,966 Bytes
07e4247 fbb72c8 0a2727f fbb72c8 0a2727f fbb72c8 e7cc0c9 fbb72c8 e9f5a3d 708a34a e9f5a3d 02a7656 e9f5a3d 02a7656 e9f5a3d 02a7656 e9f5a3d 02a7656 e9f5a3d e7cc0c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
---
license: cc-by-4.0
---
<center>
<img src="demo/logo.png" width=85%>
</center>
# SEN2NAIP
The increasing demand for high spatial resolution in remote sensing imagery has led to the necessity of super-resolution (SR) algorithms that
convert low-resolution (LR) images into high-resolution (HR) ones. To address this need, we introduce SEN2NAIP, a large remote sensing dataset
designed to support conventional and reference-based SR model training. SEN2NAIP is structured into two components to provide a broad spectrum
of research and application needs. The first component comprises a cross-sensor dataset of 2,851 pairs of LR images from Sentinel-2 L2A and HR
images from the National Agriculture Imagery Program (NAIP). Leveraging this dataset, we developed a degradation model capable of converting NAIP
images to match the characteristics of Sentinel-2 imagery (S2like). Subsequently, this degradation model was utilized to create the second component,
a synthetic dataset comprising 17,657 NAIP and S2like image pairs. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates
the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 satellite imagery.
# DOWNLOAD DATASET
```
from huggingface_hub import hf_hub_download
# Donwload cross-sensor dataset
hf_hub_download(
repo_id="isp-uv-es/SEN2NAIP",
repo_type="dataset",
filename="cross-sensor/cross-sensor.zip"
)
# Donwload synthetic dataset
for i in range(1, 19):
hf_hub_download(
repo_id="isp-uv-es/SEN2NAIP",
repo_type="dataset",
filename="synthetic/synthetic_%02d.zip" % i
)
```
# REPRODUCIBLE EXAMPLES
## Load cross-sensor dataset
```{python}
import rioxarray
import torch
DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"
cross_sensor_path = DEMO_PATH + "cross-sensor/ROI_0000/"
hr_data = rioxarray.open_rasterio(cross_sensor_path + "hr.tif")
lr_data = rioxarray.open_rasterio(cross_sensor_path + "lr.tif")
hr_torch = torch.from_numpy(hr_data.to_numpy()) / 255
lr_torch = torch.from_numpy(lr_data.to_numpy()) / 10000
```
## Load Synthetic dataset
Available methods: **vae_histogram_matching**, **vae_histogram_matching**, **gamma_multivariate_normal_90**, **gamma_multivariate_normal_75**, **gamma_multivariate_normal_50**,
**gamma_multivariate_normal_25**, **gamma_multivariate_normal_10**.
```{python}
import opensr_degradation
import rioxarray
import datasets
import requests
import tempfile
import torch
import json
def load_metadata(metadata_path: str) -> dict:
tmpfile = tempfile.NamedTemporaryFile(suffix=".json")
with requests.get(metadata_path) as response:
with open(tmpfile.name, "wb") as file:
file.write(response.content)
metadata_json = json.load(open(tmpfile.name, "r"))
return metadata_json
DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"
# Synthetic LR and HR data ------------------------------
synthetic_path = DEMO_PATH + "synthetic/ROI_0001/"
hr_early_data = rioxarray.open_rasterio(synthetic_path + "early/01__m_4506807_nw_19_1_20110818.tif")
hr_early_torch = torch.from_numpy(hr_early_data.to_numpy()) / 255
hr_early_metadata = load_metadata(synthetic_path + "late/metadata.json")
lr_hat, hr_hat = opensr_degradation.main.get_s2like(
image=hr_early_torch,
table=hr_early_metadata["sim_histograms"],
model="gamma_multivariate_normal_50"
)
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 3, figsize=(10, 5))
ax[0].imshow(hr_early_torch[[3, 1, 2]].permute(1, 2, 0))
ax[0].set_title("NAIP")
ax[1].imshow(hr_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[1].set_title("NAIPhat")
ax[2].imshow(lr_hat[[3, 1, 2]].permute(1, 2, 0)*3)
ax[2].set_title("S2like")
plt.show()
```
<center>
<img src="https://github.com/ESAOpenSR/opensr-degradation/assets/16768318/c88fa16e-bbe7-4072-b518-5ab3b7278893" width=75%>
</center>
# CITATION
TODO! |