Rasterio De Noise

Caroline Mae

2 min read

·

01-01-2025

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Rasterio, a powerful Python library for working with geospatial raster data, doesn't natively offer de-noising functionalities. De-noising, the process of removing unwanted noise from an image, typically requires separate image processing techniques. However, Rasterio seamlessly integrates with other libraries that provide these capabilities. This guide explores how to effectively de-noise raster data using Rasterio in conjunction with suitable image processing libraries.

Understanding Noise in Raster Data

Before diving into de-noising, it's crucial to understand the types of noise present in your raster data. Common types include:

• Salt-and-pepper noise: Randomly distributed pixels with extremely high or low values.
• Gaussian noise: Noise following a normal (Gaussian) distribution, appearing as random variations in pixel intensity.
• Speckle noise: A type of multiplicative noise common in radar imagery, characterized by a granular appearance.

The choice of de-noising technique heavily depends on the dominant noise type in your data.

De-noising with Scikit-image

Scikit-image is a popular Python library offering a rich set of image processing algorithms, including several effective de-noising filters. Here's how to integrate it with Rasterio:

import rasterio
from rasterio.plot import show
from skimage import restoration
import numpy as np

# Open the raster dataset with Rasterio
with rasterio.open("noisy_raster.tif") as src:
    image = src.read(1)
    profile = src.profile

# Apply a de-noising filter (example: Gaussian filter)
denoised_image = restoration.denoise_tv_chambolle(image, multichannel=False)


# Update the metadata and save the de-noised raster
profile.update(dtype=rasterio.uint8) #Adjust dtype as needed

with rasterio.open("denoised_raster.tif", 'w', **profile) as dst:
    dst.write(denoised_image.astype(rasterio.uint8), 1)

This example uses restoration.denoise_tv_chambolle, a total variation filter suitable for removing noise while preserving edges. Scikit-image also offers other filters like median filtering (skimage.filters.median) and Gaussian filtering (skimage.filters.gaussian). Experimentation is key to finding the optimal filter for your specific dataset. Remember to adjust the dtype in the profile update to match your data type.

Other Libraries and Advanced Techniques

Beyond Scikit-image, other libraries like OpenCV and Mahotas provide advanced de-noising algorithms. For more complex noise patterns or specialized applications, consider exploring these alternatives. Advanced techniques such as wavelet denoising or more sophisticated methods may be necessary for challenging cases.

Conclusion

While Rasterio doesn't directly handle de-noising, its seamless integration with libraries like Scikit-image makes the process straightforward. Remember to carefully consider the type of noise present in your data and select the appropriate de-noising technique accordingly. Always visually inspect the results to ensure the chosen method effectively removes noise without introducing unwanted artifacts.