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Robust Color Matching of Geospatial Data: An Alternative to Histogram Matching


Color balancing algorithms such as histogram matching are often applied to remote sensing data to make images of the same area taken at different times appear visually consistent with a reference image. However, if the input data differs from the reference image due to clouds, snow, or other issues, existing color balancing methods can produce severe artifacts. We introduce a new method, implemented using the Scipy stack, that fits a smooth color transfer function based on coregistered points and a-priori knowledge of the approximate white balance for the image. This approach provides color consistency with the reference image without introducing visually unrealistic artifacts when clouds and snow are present.


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