Resampling Raster Images to Lower Resolution Using Weighted Averages

What will you learn?

In this comprehensive guide, you will delve into the world of raster image processing in Python. You will learn how to effectively reduce the resolution of raster images using weighted averages, ensuring that the downscaled version accurately represents the original data. By leveraging libraries like GDAL or rasterio and incorporating numpy for array operations, you will master the art of resampling raster images with precision and efficiency.

Introduction to Problem and Solution

Working with raster images often necessitates reducing their resolution for various purposes such as optimizing file sizes or preparing data for analysis at a coarser scale. The challenge lies in resampling a raster image to a lower resolution while maintaining data integrity. Can we achieve this by applying weighted averages to constituent pixels? The answer is yes! This guide explores how Python empowers us to accomplish this task seamlessly within the realm of geospatial analysis.

By harnessing the capabilities of libraries like GDAL or rasterio alongside numpy for array manipulations, we can elegantly downsample our raster data. Through the process of aggregating pixel values from high-resolution images into larger “super pixels” in lower-resolution target images using weighted averages, we ensure that crucial details are preserved even after scaling down.

Short Intro

Discover how Python tools and libraries can be utilized to resample raster images by employing weighted averages. This approach allows you to maintain accuracy while decreasing resolution, striking a balance between efficiency and fidelity in your data processing pipeline.


import numpy as np
import rasterio
from skimage.transform import resize

def resample_raster_with_weighted_average(input_raster_path, output_raster_path, scale_factor):
    # Open the input raster file 
    with as src:
        data =,
                                   int(src.height * scale_factor),
                                   int(src.width * scale_factor)),

        # Update metadata for output file based on scaling factor 
        kwargs = src.meta.copy()
            'height': int(src.height * scale_factor),
            'width': int(src.width * scale_factor),
            'transform': src.transform * src.transform.scale((1 / scale_factor), (1 / scale_factor))

    # Write scaled data to output path 
    with, 'w', **kwargs) as dst:

# Example usage:
resample_raster_with_weighted_average('path/to/input.tif', 'path/to/output.tif', 0.5)

# Copyright PHD


The provided solution showcases how Python’s rasterio library along with numpy and skimage can be leveraged to downscale a raster image efficiently. By utilizing bilinear interpolation during reading operations, pixel values are effectively aggregated through implicit weighted averaging. Metadata adjustments based on scaling factors ensure that essential details are maintained before writing out the processed image.

This method guarantees that when pixels are combined into larger units due to reduced resolution, their values reflect an average influenced more significantly by nearby pixels than distant ones�a concept ingrained in bilinear interpolation’s weighted behavior.

    What is resampling concerning processing rasters?

    Resampling involves altering the spatial resolution of a raster dataset�either increasing (upsampling) or decreasing (downsampling) pixel sizes.

    Why would one want to decrease a raster’s resolution?

    Reducing resolution may be necessary for reasons like enhancing processing speed or analyzing landscape features at broader scales.

    What are common methods used for resampling?

    Popular methods include nearest neighbor, bilinear interpolation, cubic convolution among others�each tailored for specific applications based on desired outcomes.

    Is there any loss of information when downsizing a raster?

    Yes, downsizing leads to loss of detail as multiple pixel values merge into one; however techniques like weighted averaging aim at minimizing this effect by retaining essential information.

    Can upsampling enhance my dataset’s detail level?

    Upsampling increases pixel count but doesn’t introduce new information; it interpolates existing values leading potentially smoother transitions without adding extra details.

    How does bilinear interpolation work concerning rasters?

    Bilinear interpolation calculates an output pixel value by performing linear interpolations first in one direction then another�taking four nearest neighboring pixels into account through distance-weighted averaging implicitly.

    Are there specialized Python libraries for geospatial analysis?

    Indeed! Libraries like GDAL/OGR suite , PyProj , Geopandas etc., offer robust tools designed specifically for handling geospatial data manipulation including rasters .

    ### Does altering projection impact my need/resolution ratio ? Absolutely , since different projections distort geographical space differently; thus impacting perceived density/spacing pixels necessitating adjustments resolutions accordingly maintain integrity analyses .

    ### When should I prefer cubic convolution over bilinear interpolation ? Cubic convolution is often preferred in scenarios requiring smooth gradations because it considers influence sixteen surrounding instead just four providing smoother yet potentially more computationally intensive outcome .

    ### Can these principles applied multispectral imagery too ? Certainly ! same principles apportioning weights based proximities apply across spectral bands allowing comprehensive treatment multispectral datasets similarly maintaining balance between detail preservation computational efficiency .


    Rescaling rasters via weighted averages presents an intelligent approach to preserving essential characteristics of original datasets while adapting to constraints imposed by reduced spatial resolutions. By judiciously applying suitable algorithms and combinations thereof within the robust Python ecosystem available for geospatial analysis, practitioners equip themselves adeptly navigate complexities inherent in manipulating large-scale environmental imagery. This ensures that outcomes remain both accurate and meaningful despite underlying modifications raw datasets undergo during analytical pursuits.

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